Spring Into Bioinformatics

Differential expression

• TOC {:toc}

Session Themes and Outcomes

In this session we’re going to learn a few things about performing a differential expression analysis on gene expression data in R.

At the end of this session, you should be able to do the following tasks:

1. Read a counts table into R including:

• set the required delimiter
• selectively remove unwanted columns
• tidy up the column names

2. Explore basic data paramaters such as library size
3. Create a DGEList object
4. Assess sample similarity and identify outliers using Unsupervised Clustering
5. Perform a simple differential expression analysis

Data setup

Data QC

Estimation of library sizes

PCA/MDS assessment of outliers and batch effects

Differential expression

Now that we have our data in a nicely formatted file, we can move to R and follow a fairly standard workflow. Along the way, we’ll come across a few useful packages, data structures and coding tricks which will be applicable in many other contexts.

The packages we’ll use for this include not only the tidyverse, but a few other packages which are hosted on Bioconductor, such as limma, edgeR and AnnotationHub. We’ll also add the magrittr,pander and scales packages as they contain some useful additional utilities.

library(limma)
library(Glimma)
library(edgeR)
library(AnnotationHub)
library(tidyverse)
library(magrittr)
library(scales)
library(pander)
library(ggrepel)
library(RColorBrewer)

Data Setup

Import

First we should import the file we created using featureCounts. Please take a moment to check the location of the counts.out file you created in the last session to ensure the assignment of the location to the file object is correct.

file <- "../data/2_alignedData/featureCounts/counts.out"
rawCounts <- read_delim(file, comment = "#", delim = "\t")

This file has the gene identifiers in the first column, followed by the chromosome number, start, end, strand and length information (recall that we have subset our data to only include mouse chromosome 1), with the remaining columns containing the name of the bam file and the number of reads aligning to each gene. The first thing we might like to do is to remove the columns which don’t contain the count data we require today, and tidy up those column names.

countData <- dplyr::select(rawCounts, -c(Chr, Start, End, Strand, Length)) %>%
  set_colnames(basename(colnames(.))) %>%
  set_colnames(str_remove(colnames(.), ".trimmedAligned.sortedByCoord.out.bam"))

That looks much cleaner and we haven’t lost any important information but we’re not quite done. The group information we need for the differential expression is locked in the samplename. Next we can split the sample ID from the group information and save that information in a dataframe for later use.

sampleGroups <- as.data.frame(names(countData[-1]), stringsAsFactors = FALSE) %>%
  set_colnames(., "samplename") %>%
  tidyr::separate(., col = samplename, into = c("samplename", "group"), remove = TRUE)

Now we have a small table of metadata regarding our samples. We can also tidy up our countData table column names a little more by moving the gene IDs to row names and removing the group information after the sample name.

countData <- as.data.frame(countData) %>%
  column_to_rownames("Geneid")
colnames(countData) <- sampleGroups$samplename

Create a DGE List

The main object type we like to use for differential gene expression is a DGEList, which stands for Digital Gene Expression List, and is an object used by edgeR to store count data. This object is more complex than others we have looked at in R so far because it contains a number of slots for storing not only our data, but also different paramaters about the data. The DGEList objects have two mandatory elements, with the first being our counts data, and the second being our sample information. Recall that above we have made the gene IDs the row names and the sample IDs the column names.

Here’s one way to create a DGEList.

dgeList <- countData %>%
    DGEList() %>%
    calcNormFactors()

Here we have taken our counts table with gene IDs as row names and sample IDs as column names and put it in to a DGEList object, called dgeList, and called the calcNormFactors() function to calculated the normalisation factors, which can be seen in dgeList$samples.

If we decided to fit our counts using a negative binomial model (for discrete data), these norm.factors would be included in the model to adjust for variations in the library size and count distributions (for an interesting post on what “fitting” a model is see [fit] (https://diamondage.com/2017/06/03/what-does-it-mean-to-fit-a-model-anyway/)).

We have chosen to use a different approach for our analysis today, but taking the time to include the calculation of normalisation factors step as we form the DGEList object is still good practice as it allows us to change methodology part way through our planned analysis if we determine the negative binomial model is better suited to our data.

Take a moment now to run this next code chunk and inspect the samples data frame sitting inside the DGEList object we have just created.

dgeList$samples

R understood that the column names from our counts table contained the sample IDs for our project and created the samples data frame inside the DGEList object. lib.size and norm.factors have also been calculated from the counts table and incorporated into the element. We haven’t yet included any information describing our samples. In the samples element, we can set the group variable we previously took from the sample IDs by running:

dgeList$samples$group <- sampleGroups$group %>%
    factor(levels = c("cbc", "skm"))

Take another moment now to inspect the samples data frame again and you will now see what was previously a row of 1s is now populated with the appropriate tissue type.

dgeList$samples

As a small note of warning, keep in mind that we did not at any time alter the order in which our samples were being reported. This allowed us to simply cut the information from one table and paste it into another. There are ways of using mutating joins to ensure order is never corrupted including left_join, right_join, inner_join and others.

It is always worth taking the time to perform a “sanity check” to ensure the sample names and groups are correctly aligned before you continue.

Add Gene Information

A common, but optional element that we can include in a DGEList, is one called genes. This is where we can place information such as the genomic location, the gene symbol and anything else we think is going to be relevant. First, we need to find this information though, and we’ll use the package AnnotationHub which contains the metadata for all of the annotation packages included in Bioconductor.

ah <- AnnotationHub()

The structure of this new object ah is quite advanced. Take a moment to type in the object name and run the line, you’ll get an informative summary of all available annotation packages.

ah

Note that we have numerous data providers, species and data classes. The column on the left (AH####) is a shorthand ID we can use to retrieve any of these annotation objects.

We can also subset the Annotation Hub object to help us find what we’re looking for. In the following code, we’re restricting our search to mouse, and specifying Ensembl as our data source.

The final line looks for an object of class EnsDb which is a database object containing a large amount of information, but in a relatively user-friendly way.

ah <- ah %>%
  subset(species == "Mus musculus") %>%
  subset(dataprovider == "Ensembl") %>%
  subset(rdataclass == "EnsDb")

We have now subset ah and removed most of the information we don’t need (ie infromation related to other species and other data providors). The step is to ensure we are only using data from an appropriate release. ah currently contains data from release 87-92, which will be close enough to match the data we have generated so far (which was Ensembl 93). Ideally, these should be the same, but there is no EnsDb object for Ensembl 93. I’m sure one will be made available in the next release of Bioconductor. When we inspected ah above we saw that the latest release was AH60992 | Ensembl 92 EnsDb for Mus Musculus. Today we will use this annotation record and define it as the object ensDb

ensDb <- ah[["AH60992"]]
ensDb

There are many helper functions for extracting data from this package, such as transcripts(), promoters() and genes(). We want gene information today, so let’s just use that function. Note that after we obtain the data, we’re using subset() to ensure we only keep the data from chromosome 1.

genesGR <- genes(ensDb) %>%
    subset(seqnames == 1)

This object is a GenomicRanges (or GRanges) object and these are the bread & butter of genomic analysis in R. We could spend hours just looking at these, but the main point is that on the right of the gene IDs we have the chromosome (seqnames) followed by the range of bases the genes is contained within and the strand the gene is located on (note that start and end positions are both 1-based relative to the 5’ end of the plus strand of the choromosome, even when the range is on the minus strand). This is the core GRanges element. Run the following code chunk to return this information using the function granges()

granges(genesGR)

Underlying each GRanges object is a seqinfo object and these contain all of the genomic information about chromosome names and lengths. If comparing two GRanges objects, they must have identical seqinfo objects otherwise the comparison will return an error. This actually makes perfect sense, but can create issues when comparing data from UCSC, NCBI and Ensembl as they all use different formats for their chromosome names, even though they’re based on the same assemblies.

seqinfo(genesGR)

To the right of the pipes, we have the metadata columns, accessed using the function mcols(). Notice this returns a DataFrame which is a slightly more controlled version of a data.frame. The differences are beyond the scope of this course, but they can easily be coerced to a data.frame using as.data.frame. If you want to use the dplyr functions on them, you will need to go through this step.

mcols(genesGR)

Before we place this information into the genes element of our DGEList object we will remove columns that are redundant for our purposes today, and keep only the four most useful ones. We can achieve this by calling the mcols() function on our GRanges object. Firstly we use the square brackets to subset the genesGR object we created earlier, and then by using the assignment operator (<-) to overwrite this subsetted element back into the object.

mcols(genesGR) <- mcols(genesGR)[c("gene_id", "gene_name", "gene_biotype", "entrezid")]

If we wish to add this to our DGEList, note that the order of genes will be completely different. To fix this, we will use the rownames of our DGEList object to reorder the genes object. Taking the time to ensure that the two gene lists are in the same order is important because R will give no warning or error to inform us if the two gene lists are in different orders. R will not check that these two objects are compatible.

dgeList$genes <- genesGR[genesGR$gene_id %in% rownames(dgeList),]

Here we have used square brackets [] to subset and reorder the genesGR object such that it now only contains the genes in our dgeList object, and in the same order.

Now when we subset our DGEList by gene, the genes element will also be subset accordingly and the initial relationships will be maintained. Take a moment to inspect the first four elements of our dgeList object by running the code chunk below.

dgeList[1:4,]

You will notice that the degList$genes element is now populated with the information from annotationHub we downloaded earlier, and that dgeList$counts and degList$genes are in the same order.

Data QC

Undetectable genes

You may have noticed that no reads aligned to a number of the genes contained in this dataset. As common practice we remove genes with zero counts across all samples before we continue with our initial data exploration. Rather than inspect our data one line at a time looking for zeros we can use R to perform a logical test to see how many genes were not detected in our dataset. First we’ll add up the total counts for every gene and see how many received at least one count. In the following code chunk we will take the counts element from our DGEList object and pipe it into the function rowSums() which will output a named number vector with a single value (the sum of each gene) for each gene. This vector is in turn piped into another function is_greater_than() which will ask the logical question “is the value greater than the given value”, with the given value being 0. We then pipe the TRUE/FALSE answers to this logical question into table which will provide us with a simple two number summary.

dgeList$counts %>% 
    rowSums() %>%
    is_greater_than(0) %>%
    table

Any genes that fail our test (ie the sum of which are not greater than zero) will be counted in the FALSE column, and any genes that pass our test (ie the sum of which is greater than zero) will be counted in the TRUE column. We can see that a proportion of genes were not expressed or were undetectable in our original samples. A common approach would be to remove undetectable genes using some metric, such as Counts per Million reads, also known as CPM. We could consider a gene detectable if returning more than 1CPM, in every sample, from one (usually the smaller), of the treatment groups.

Although our dataset is small (6/8 libraries are < 1e6 reads), we usually deal with libraries between 20-30million reads. In the context of the more usual library size a CPM of 1 equates to 20-30 reads aligning to a gene, which we test in every sample from a treatment group. Here our smallest group is 4 so let’s see what would happen if we applied the filter of > 1 CPM in 4 samples.

First we’ll calculate the CPM for each gene in each sample, and then we’ll apply a logical test to each value checking whether it’s greater than 1. Next, we’ll add these up for each gene (i.e. row) and this will give us the total number of samples for each gene, that passed our criteria of CPM > 1. Finally, we’ll check for genes which passed our criteria in more than 4 samples, as our smallest group is 4. We could also have used the function is_weakly_greater_than() which would test for equality (\(\geq\)) instead of strictly greater than (\(>\)).

dgeList %>%
    cpm() %>%
    is_greater_than(1) %>%
    rowSums() %>%
    is_greater_than(4) %>%
    table()

Losing about 1/3 of the genes is pretty common, so let’s now apply that to our dataset. The object genes2keep below will be a logical vector deciding on whether we keep the gene for downstream analysis, based purely on whether we consider the gene to be detectable. We’ll create a new DGEList object by subsetting our primary one using the [] trick we learned earlier. We are creating a new object here so that if we change our mind about our filtering strategy, we don’t have to rerun all the code above.

genes2keep <- dgeList %>%
    cpm() %>%
    is_greater_than(1) %>%
    rowSums() %>%
    is_greater_than(4)

dgeList_Filtered <- dgeList[genes2keep,] %>%
  calcNormFactors()

Let’s compare the distributions of the two datasets (cbc and skm), using CPM on the log2 scale. In the following, the command par(mfrow = c(1,2)) is a base graphics approach and sets the plotting parameters to be a multi=feature layout, with 1 row and 2 columns. We set these paramaters because the convenient function plotDensities() we are calling below uses base graphics, not ggplot2.

col <- brewer.pal(nrow(dgeList$samples), "Paired")
par(mfrow = c(1,2))
dgeList %>%
    cpm(log = TRUE) %>%
    plotDensities(legend = FALSE,
                  main = "A. Before Filtering",
                  col=col)
dgeList_Filtered %>%
    cpm(log = TRUE) %>%
    plotDensities(legend = FALSE,
                  main = "B. After Filtering",
                  col=col)

Comparison of logCPM distributions before (A) and after (B) filtering for undetectable genes. Values on the x-axis represent logCPM

par(mfrow = c(1,1))

Note the peak at the left in the first plot around zero. This is all of the genes with near-zero counts. Then note that this peak is missing the second plot, confirming that we have removed most of the undetectable genes. Ideally we would like to see that the peak around zero has been removed after filtering, and that the samples all have approximately the same distribution of counts. Keeping in mind that we have significantly subset our original fastq data during alignment earlier such that we only have genes from mouse chromosome 1.

Library Sizes

Next we should check our library sizes. It does appear that the two tissue types have a different profile of reads. There could be a number of reasons for seeing differences between groups including sequencing batch effects, differences in the RNA quality from different tissues or extractions, or lab operations being performed by different staff. This is not ideal but most modelling approaches will be able to handle this.

dgeList_Filtered$samples %>%
    ggplot(aes(group, lib.size, fill = group)) +
    geom_boxplot() +
    scale_y_continuous(labels = comma) +
    labs(x = "Tissue Type", y = "Library Size") +
    theme_bw() 

Library Sizes after filtering for undetectable genes.

We’ve used he function comma from the scales package here to help us interpret the y-axis. For most vertebrate datasets we expect libraries of >20million reads, but as we’ve given you a significantly reduced dataset, these numbers are pretty acceptable.

Unsupervised clustering of samples

One of the first steps in exploring our gene expression data is that of unsupervised clustering in the form of a multi-dimensional scaling plot (MDS), Principal Component Analysis (PCA) or similar. By unsupervised here we mean to say that we haven’t told R how many groups we are expecting to see, or given any information about which samples we expect to see clustering closely together. Visually inspecting the data in this way allows us to start to understand the extent to which any differential expression can be detected before we have performed any statistical tests. It may also help us to identify any outliers in our samples, or perhaps a factor we haven’t yet considered that should be included when fitting our model later.

Multi-dimensional Scaling Plot (MDS)

An important first exploration of our sequencing data is the MDS plot.

The plotMDS() function from the limma package creates a temporary log fold change between pairs and plots samples on a two-dimensional scatterplot so that distances on the plot approximate the typical \(log_2\) fold changes between the samples.

A simplified example of how we calculate the logFC for our MDS plot.

1. For a given counts table

	sample_1	sample_2	sample_3
Gene_1	3.0	0.25	2.8
Gene_2	2.9	0.8	1.0
Gene_3	2.2	1.0	1.5

….

2. We take our gene counts and calculate the logFC between each pair of genes

• logFC Gene_1: sample_1 versus sample_2 \(= log(\frac{3}{.25}) = 3.58\)
• logFC Gene_2: sample_1 versus sample_2 \(= log(\frac{2.9}{.8}) = 1.86\)
• Continue calculating the logFC between each pair of genes

3. Lastly, take the average of the absolute value of the logFC among genes

• nb: we take the absolute value so the negative log fold changes don’t cancel out the positive ones

4. We can now use these distances between samples to plot our MDS.

Thankfully we don’t need to perform all of these calculations by hand, or even write our own script to do so

plotMDS(cpm(dgeList_Filtered, log = TRUE))
title(main = "Multi Dimensional Scaling Plot \nMouse Chromosome 1")

MDS showing two clear groups in the data. limma::plotMDS

Here we see that the Leading logFC on dimension 1 appears to seperate the samples by tissue type suggesting that as we set out to investigate - there is a difference in gene expression between cerebellar cortex (cbc) and skeletal muscle (skm).

WARNING: it is easy to observe the difference between our groups on the MDS plot and assign them directly to tissue type. The files we are using here are part of a much larger research effort and as such there is much information not being assessed. eg. were our samples all sequenced at the same time, on the same machine? The differences we observe could be due to something we call batch effects.

Batch effects can be defined as systematic non-biological variation between groups of samples (or batches) due to experimental artifacts. Batch effects are a complication of high-throughput studies which occur when measurements are affected by laboratory conditions. The topic of batch effects and how to account for them deserves its own session but we don’t have the time to delve into that here. If you are interested in reading more about batch effects and how to overcome them the rna-seqblog has a number of interesting articles discussing the topic.

We can use the plotMDS() function directly to create an MDS plot as we did above, or assign the data created to an object that we can pipe into ggplot for a presentation ready figure.

mds <- dgeList_Filtered %>%
  cpm(log = TRUE) %>%
  plotMDS()

yes

The following chunk wraps the plotting of our MDS plot inside plotly::ggplotly() which takes a ggplot object and makes it interactive. You may notice that label was included as a plotting aesthetic, but no labels were added as a layer in ggplot2. These will instead be added once the plot is made interactive (try hovering the mouse over one of the points on the plot to see what we mean by interactive). Interactive plots will only be interactive in the Viewer tab or when compiling an RMarkdown document to an html format, and will remain static if rendering to an MS Word document or pdf.

plotly::ggplotly(
  as.data.frame(cbind(dim1=mds$x,dim2= mds$y)) %>%
        rownames_to_column("sample") %>%
        as_tibble() %>%
        left_join(rownames_to_column(dgeList_Filtered$samples, "sample")) %>%
        ggplot(aes(x = dim1, y = dim2,
                   colour = group,
                   label = sample)) +
    xlab("Leading logFC Dim 1") +
    ylab("Leading logFC Dim 2") +
        geom_point(size = 3) +
        theme_bw()
)

MDS showing two clear groups in the data

In the spirit of interactive plots available in R, run the following code chunk to create an interactive MDS plot as an HTML page using Glimma::glMDSPlot. If you set \(launch=TRUE\) this code chunk will open a new browser window containing an MDS plot in the left panel and a bar plot showing the proportion of variation explained by each dimension in the right panel.

dgeList_Filtered %>%
  cpm(log=TRUE) %>%
  glMDSPlot(., labels=rownames(dgeList_Filtered$samples),
            groups=dgeList_Filtered$samples$group,
            launch=TRUE)

Tasks

1. Try clicking on the bars of the bar plot to change the pair of dimensions being plotted on the MDS.
2. What might account for the variation between dimension 2 and dimension 3?

PCA

Next we might choose to perform a Principal Component Analysis on our data, commonly abbreviated to PCA. This time, let’s take our CPM values & asses them on the log2 scale to make sure our PCA results are not heavily skewed by highly expressed genes. Our first step is to create a new object to hold the PCA results. In the following code chunk we take our filtered DGEList obeject dgeList_Filtered and pipe it to the cpm() function we used earlier to calculate the CPM values for each gene prior to setting the filtering threshold. We have made one small change here by including \(log - TRUE\) in the function call. By default this is \(log_2\). Next we pipe the \(log2\)CPM values into the t() function. This will transpose the matrix of counts such that the matrix now has genes in the columns and samples in the rows, which we then pipe into prcomp from the stats package. prcomp() returns a list with class “prcomp” containing a number of useful components.

To find out more about prcomp try typing help("prcomp") into the console. This will open the R Documentation for this function in the Help tab.

pca <- dgeList_Filtered %>%
    cpm(log = TRUE) %>%
    t() %>%
    prcomp() 

In our DGEList, we have the genes as the variables of interest for our main analysis, however for the PCA we’re looking at out samples as the variables of interest. The third line in the above code chunk has transposed (t()) the matrix returned by cpm(log = TRUE) to place the samples as the rows, which is where the function prcomp() expects to see the variables of interest.

Run the next code chunk for a quick inspection of the results shows that the first two components capture most of the variability, as expected. Beyond this observation, the details of PCA are beyond what we can cover here.

summary(pca)$importance %>%
  pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
Standard deviation	52.73	15.1	12.08	11.1	8.862	8.448	7.239	1.786e-14
Proportion of Variance	0.799	0.0655	0.04194	0.03539	0.02257	0.02051	0.01506	0
Cumulative Proportion	0.799	0.8645	0.9065	0.9418	0.9644	0.9849	1	1

Just as we did with the MDS plot, we can plot our PCA results to see if samples group clearly with their tissue type based on our main two principal components (ie PC1 and PC2). Much the same as the MDS plot, any clear separation of our groups of interest can be considered a positive sign that we will find differentially expressed genes.

plotly::ggplotly(
    pca$x %>%
        as.data.frame() %>%
        rownames_to_column("sample") %>%
        as_tibble() %>%
        dplyr::select(sample, PC1, PC2) %>%
        left_join(rownames_to_column(dgeList_Filtered$samples, "sample")) %>%
        ggplot(aes(x = PC1, y = PC2,
                   colour = group,
                   label = sample)) +
        geom_point(size = 3) +
        theme_bw()
)

PCA showing two clear groups in the data

Multi-Dimensional Scaling can look similar to PCA, but asks a slightly different question of the data. PCA creates plots based on correlations between samples, MDS creates plots based on distances between samples (here logFC between genes). Both methods return the results: 1. Coordinates for a graph 2. Percent of variation each axis accounts for 3. Loading scores (to determine which variables have the largest effect)

It is common to perform one or both of these analyses before starting a differential expression analysis but rest assured that usually results found under both methods will reveal similar patterns in your data.

For a great tutorial on multi-dimensional scaling check out “StatQuest: MDS and PCoA” here, and a companion vidoe about PCA titled “StatQuest: Principal Component Analysis (PCA), Step-by-Step” here.

Tasks

1. How much variation is accounted for in PC1 and PC2 combined?
2. Using the code chunk above as a template, plot PC3 and PC4 using ggplot and wrapping with plotly::ggplotly to investigate other sources of variation.

Differential Expression

In the previous sections we have worked with read counts, which are a discrete value and formally cannot be modelled using the assumption of normally distributed data (recall that counts data typically approximate a negative binomial distribution, not a normal distribution). The failure to meet the assumption of normality rules out linear models and t-tests for RNA-Seq, so many packages have been developed which use the negative binomial distribution to model these counts (e.g. edgeR and DESeq2).

An alternative was proposed by Law et al., where they apply a system of weights to the counts which allow the assumption of normality to be applied. This method is called voom and we’ll use this today.

Voom transformation and calculation of variance weights

First, lets set up a model matrix to specify the model to be fitted.

model_matrix <- model.matrix(~0 + group, data = dgeList_Filtered$samples)

The above specifies a model where each coefficient corresponds to a group mean. Importantly we have used the samples elemenet of the DGEList object to set the model matrix. By using the DGEList object we ensure that the samples (and therefore the group assignment) has been given in the same order as the counts table.

Tasks

1. Take a moment to inspect the model_matrix object. You can do this by simply typing the object name into the console and hitting the Enter key.
2. What information has been added here?
3. What could happen if the group assignment wasn’t given in the same order as the counts table?

Next we will

voomData <- voom(dgeList_Filtered, model_matrix, plot = TRUE)

yes

Note that running the voom() function without the model_matrix object will add a design matrix to the data based on the group column of the DGEList object but which we chose to specify explicitly. This is something of a style choice, but either way we can now use the voom object to perform a simple linear regression on each gene, which amounts to a t-test in this dataset.

From here it’s a simple matter to code the analysis and inspect results.

A lot of analysis is performed in the following code chunk:

1. We’ll use lmFit() to fit the model for every gene using the design matrix in voomData. Note that we’re not using CPM, but are instead fitting the counts directly, after incorporation of the voom-derived weights.
2. Next we moderate the variances using an empirical Bayes approach. This uses the assumption that our variance estimates will be a mix of overestimates and underestimates, and shrinks them all towards a central value. As a strategy, this is widely accepted and has been shown to increase power and reduce false positives.
3. Finally, we’ll create a list of all genes with a summary of the results which we then convert to a tibble. The last step will remove the gene IDs (i.e. the row names), but because we’ve included our gene information in the DGEList object, this will be added to the results and we’ll still know which gene is which (now we see why we needed to ensure the genesGR object was added in the same order as the countData object).

topTable <- voomData %>% 
    lmFit() %>%
    eBayes %>%
    topTable(coef = "groupskm", n = Inf) %>%
    as_tibble()

Recall from our model_matrix that we are comparing groupcbc with groupskm (we could rename these if we wanted to but for our purpose here we can leave them as is). This means that when we call the coefficient groupskm in the topTable function we are using the column name to specify which coefficient or contrast of the linear model is of interest

Note that the GRanges information has been coerced into columns to form a data.frame/tibble and that looks a bit messy. To tidy things up, the following code will join all of the genomic co-ordinates into a single column, rename a few columns on the fly and by not asking for ID.width & ID.gene_biotype, we’ve saved ourselves dealing with a few redundant columns. We could actually add this to the chunk above to save forming then modifying an R object, but it’s been left separate for clarity.

Tasks

1. Take a quick look at the topTable object before and after the tidy up by typing topTable %>% head() into the console and hitting the Enter key.

topTable <- topTable %>%
    unite("Range", ID.start, ID.end, sep = "-") %>%
    unite("Location", ID.seqnames, Range, ID.strand, sep = ":") %>%
    dplyr::select(Geneid = ID.gene_id, 
                  Symbol = ID.gene_name,
                  AveExpr, logFC, t, P.Value, 
                  FDR = adj.P.Val, 
                  Location, 
                  Entrez = ID.entrezid)

Now that we have our ranked list of genes, we should really inspect these visually. A commonly used plot is a volcano plot, where the position of values on the x-axis is the logFC estimates for each gene, and the position on the y-axis relates to the strength of statistical significance for each gene. Before plotting, we added a simple column called DE to indicate whether we considered a gene to be differentially expressed (ie. the mean between the groups is different), based purely on an FDR-adjusted p-value < 0.05. To add some colour to the plot (and to make identifying the DE genes easy) we can use this DE status to colour points.

topTable %>%
    mutate(DE = FDR < 0.05) %>%
    ggplot(aes(logFC, -log10(P.Value), colour = DE)) +
    geom_point(alpha = 0.5) +
    # geom_text_repel(data = . %>%
    #                     dplyr::filter(DE) %>%
    #                     dplyr::filter(-log10(P.Value) > 4 | abs(logFC) > 2.5),
    #                 aes(label = Symbol)) +
    # scale_colour_manual(values = c("grey", "red")) +
    theme_bw() +
    theme(legend.position = "none")

Volcano plot showing DE genes between the two tissue types.

So what have we done here. Firstly we took our topTable object that contains the logFC values and FDR for each gene and piped the object into mutate. Here we have used mutate to create a new column called DE based on the logical question is the value in the FDR column less than 0.05?. For each row in our table if the value of the FDR column is < 0.05 a TRUE value will be returned, and of course a value of FALSE if \(\geq\) 0.05. As another perspective, we might like to see whether these genes are at the high or low end of the range for expression values. This is often referred to as an MD plot, which stands for Mean expression vs Difference, where difference is more commonly referred to as logFC. We’ve added an arrange() call in the following to ensure our DE genes are plotted last and are the most visible in our figure.

topTable %>%
    mutate(DE = FDR < 0.05) %>%
    arrange(desc(P.Value)) %>%
    ggplot(aes(AveExpr, logFC, colour = DE)) +
    geom_point(alpha = 0.5) +
    geom_text_repel(data = . %>%
                        dplyr::filter(DE) %>%
                        dplyr::filter(abs(logFC) > 2 | AveExpr > 14),
                    aes(label = Symbol)) +
    scale_colour_manual(values = c("grey", "red")) +
    labs(x = "Average Expression (log2 CPM)",
         y = "log Fold-Change") +
    theme_bw() +
    theme(legend.position = "none")

Mean-Difference plot showing fold-change potted against expression level. Genes considered as DE are highlighted in red.

If were happy with our results, we could exprt this list using write_csv() or write_tsv(). We could also produce a short table summarising the “big guns” in the dataset. As there are more than 100 genes considered as DE here, let’s restrict to those with logFC beyond the range \(\pm1\), which equates to 2-fold up or down-regulation.

topTable %>%
    dplyr::filter(FDR < 0.05, abs(logFC) > 1) %>%
    dplyr::select(ID = Geneid, Symbol, AveExpr, logFC, P.Value, FDR) %>%
    pander(caption = paste("The", nrow(.), "most DE genes when ranked by p-value, and filtered on a logFC beyond the range $\\cpm1$"))

The 1111 most DE genes when ranked by p-value, and filtered on a logFC beyond the range \(\cpm1\)

ID	Symbol	AveExpr	logFC	P.Value	FDR
ENSMUSG00000034066	Farp2	12.8	13.78	6.233e-21	1.774e-18
ENSMUSG00000033021	Gmppa	11.62	16.6	1.479e-20	1.774e-18
ENSMUSG00000088669	Gm25336	14.83	15.22	1.368e-20	1.774e-18
ENSMUSG00000099382	Gm28631	13.7	14.05	9.073e-21	1.774e-18
ENSMUSG00000102278	Gm37145	14.99	15.4	3.241e-20	2.412e-18
ENSMUSG00000103617	Gm37950	12.12	12.86	9.966e-21	1.774e-18
ENSMUSG00000102276	2900069G24Rik	11.74	12.44	7.428e-21	1.774e-18
ENSMUSG00000064823	Snord82	12.16	12.38	1.205e-20	1.774e-18
ENSMUSG00000087230	Mroh3	9.395	14.99	5.368e-20	3.322e-18
ENSMUSG00000026429	Ube2t	11.83	13.15	2.353e-20	2.383e-18
ENSMUSG00000103152	Gm8790	11.02	12.29	1.572e-20	1.774e-18
ENSMUSG00000026117	Zap70	11.88	12.04	1.385e-20	1.774e-18
ENSMUSG00000103799	Gm38093	11.75	12.82	2.77e-20	2.412e-18
ENSMUSG00000099622	Gm29188	11.57	11.94	1.592e-20	1.774e-18
ENSMUSG00000064612	Gm26457	12.1	13.01	4.212e-20	2.933e-18
ENSMUSG00000043716	Rpl7	11.62	12.29	2.897e-20	2.412e-18
ENSMUSG00000103763	Gm37860	11.23	12.81	4.954e-20	3.246e-18
ENSMUSG00000049608	Gpr55	12.25	13.05	6.538e-20	3.833e-18
ENSMUSG00000060985	Tdrd5	10.73	11.94	3.248e-20	2.412e-18
ENSMUSG00000077202	Gm25612	11.64	13.04	7.764e-20	4.048e-18
ENSMUSG00000042849	Olfr1414	11.71	12.56	7.055e-20	3.93e-18
ENSMUSG00000100185	Gm28634	7.863	13.04	1.013e-19	4.386e-18
ENSMUSG00000026220	Slc16a14	12.14	12.59	7.995e-20	4.048e-18
ENSMUSG00000016526	Dyrk3	10.82	12.57	8.861e-20	4.121e-18
ENSMUSG00000094638	Gm21972	11.55	12.35	1.156e-19	4.474e-18
ENSMUSG00000025980	Hspd1	12.19	12.12	1.024e-19	4.386e-18
ENSMUSG00000025907	Rb1cc1	11.27	11.84	8.879e-20	4.121e-18
ENSMUSG00000102605	Gm37264	12.77	14.29	2.971e-19	7.195e-18
ENSMUSG00000077794	Gm25560	11.13	12.21	1.267e-19	4.704e-18
ENSMUSG00000026121	Sema4c	8.491	12.93	2.137e-19	5.806e-18
ENSMUSG00000026554	Dcaf8	12.01	12.08	1.713e-19	5.742e-18
ENSMUSG00000090637	Gm6189	12.04	12.95	2.854e-19	7.064e-18
ENSMUSG00000051285	Pcmtd1	11.1	11.36	1.086e-19	4.474e-18
ENSMUSG00000089990	Gm15852	11.42	12.35	2.423e-19	6.331e-18
ENSMUSG00000104022	Gm37214	10.87	11.29	1.165e-19	4.474e-18
ENSMUSG00000026484	Rnf2	11.74	12.37	2.54e-19	6.43e-18
ENSMUSG00000026201	Stk16	10.99	11.61	1.752e-19	5.742e-18
ENSMUSG00000101840	Gm28294	13.5	14.68	6.436e-19	1.144e-17
ENSMUSG00000061816	Myl1	10.77	12.54	3.398e-19	7.885e-18
ENSMUSG00000087820	Mir1928	12.54	14.29	6.586e-19	1.146e-17
ENSMUSG00000026494	Kif26b	12.65	12.34	3.493e-19	7.942e-18
ENSMUSG00000025917	Cops5	10.33	11.3	2.11e-19	5.806e-18
ENSMUSG00000005681	Apoa2	11.18	12.34	4.354e-19	9.327e-18
ENSMUSG00000103227	Gm37733	9.869	11.08	1.895e-19	5.806e-18
ENSMUSG00000006301	Tmbim1	10.68	10.89	1.704e-19	5.742e-18
ENSMUSG00000103705	4930518J20Rik	10.22	10.71	1.434e-19	5.153e-18
ENSMUSG00000064435	Gm25588	10.48	11.73	3.855e-19	8.421e-18
ENSMUSG00000091844	Gm8251	10.47	10.9	2.075e-19	5.806e-18
ENSMUSG00000026037	Orc2	10.79	10.65	1.818e-19	5.787e-18
ENSMUSG00000102716	Gm5099	11.17	11.89	5.271e-19	1.068e-17
ENSMUSG00000088432	Gm25384	9.929	10.76	2.444e-19	6.331e-18
ENSMUSG00000037470	Uggt1	10.35	10.55	2.043e-19	5.806e-18
ENSMUSG00000001143	Lman2l	10.71	10.82	3.041e-19	7.208e-18
ENSMUSG00000047067	Dusp28	11.74	12.92	1.058e-18	1.551e-17
ENSMUSG00000058603	Rpl28-ps1	10.52	10.46	2.118e-19	5.806e-18
ENSMUSG00000095576	Fmo6	11.6	11.11	4.619e-19	9.708e-18
ENSMUSG00000099472	Gm29539	9.791	11.31	6.346e-19	1.144e-17
ENSMUSG00000102246	9430037O13Rik	10.32	11.18	5.859e-19	1.111e-17
ENSMUSG00000064602	Snora41	10.86	11.49	8.431e-19	1.317e-17
ENSMUSG00000104376	Gm37516	11.02	11.04	6.472e-19	1.144e-17
ENSMUSG00000067851	Arfgef1	11.63	12.27	1.318e-18	1.813e-17
ENSMUSG00000103978	4930474B08Rik	10.48	11	7.128e-19	1.203e-17
ENSMUSG00000092085	Gm17224	10.66	11.24	8.621e-19	1.317e-17
ENSMUSG00000073725	Lmbrd1	10.99	10.57	4.777e-19	9.855e-18
ENSMUSG00000102860	Gm37638	11.1	11.11	8.633e-19	1.317e-17
ENSMUSG00000103948	4930594C11Rik	11.15	11.98	1.423e-18	1.887e-17
ENSMUSG00000026179	Pnkd	10.45	10.6	5.741e-19	1.111e-17
ENSMUSG00000002881	Nab1	11.59	10.85	7.918e-19	1.26e-17
ENSMUSG00000101952	Gm10550	10.79	10.54	6.211e-19	1.144e-17
ENSMUSG00000026098	Pms1	7.204	10.91	8.928e-19	1.344e-17
ENSMUSG00000065629	Gm24826	10.64	11.26	1.185e-18	1.695e-17
ENSMUSG00000010609	Psen2	11.42	10.12	3.577e-19	7.969e-18
ENSMUSG00000005501	Usp40	10.99	10.58	6.899e-19	1.182e-17
ENSMUSG00000104306	Gm36972	8.918	11.78	1.593e-18	2.054e-17
ENSMUSG00000102404	5530400K19Rik	10.79	10.36	5.437e-19	1.082e-17
ENSMUSG00000088196	Gm23902	10.69	11.31	1.299e-18	1.809e-17
ENSMUSG00000037447	Arid5a	10.72	11.19	1.202e-18	1.695e-17
ENSMUSG00000026582	Sele	11.09	10.38	5.884e-19	1.111e-17
ENSMUSG00000102833	Gm9694	10.57	10.63	7.836e-19	1.26e-17
ENSMUSG00000099969	Gm28960	10.86	10.79	1.022e-18	1.518e-17
ENSMUSG00000094394	Gm23402	10.55	11.19	1.406e-18	1.887e-17
ENSMUSG00000103744	Gm38238	10.21	10.37	7.593e-19	1.26e-17
ENSMUSG00000100021	Gm18697	10.45	11.35	1.785e-18	2.26e-17
ENSMUSG00000052534	Pbx1	10.43	11.35	1.947e-18	2.41e-17
ENSMUSG00000025903	Lypla1	9.673	10.65	1.193e-18	1.695e-17
ENSMUSG00000099771	Gm19257	11.05	11.13	1.926e-18	2.41e-17
ENSMUSG00000100620	Gm28277	10.57	11.07	2.08e-18	2.519e-17
ENSMUSG00000087886	Gm23599	10.42	11.48	2.807e-18	3.191e-17
ENSMUSG00000085857	Gm15392	10.02	10.44	1.357e-18	1.843e-17
ENSMUSG00000096968	Gm26620	10.02	9.964	7.875e-19	1.26e-17
ENSMUSG00000026617	Bpnt1	10.27	11.02	2.443e-18	2.899e-17
ENSMUSG00000046994	Mars2	10.01	10.47	1.604e-18	2.054e-17
ENSMUSG00000103644	Gm29678	9.729	10.71	2.592e-18	3.04e-17
ENSMUSG00000014980	Tsen15	10.76	11.03	3.263e-18	3.531e-17
ENSMUSG00000026499	Acbd3	10.41	10.83	2.921e-18	3.256e-17
ENSMUSG00000113084	A030003K21Rik	10.29	10.09	1.477e-18	1.935e-17
ENSMUSG00000100770	Gm28466	11.02	10.7	2.792e-18	3.191e-17
ENSMUSG00000026085	Lyg1	9.512	10.61	2.705e-18	3.139e-17
ENSMUSG00000103716	Gm19430	11.31	10.3	2.072e-18	2.519e-17
ENSMUSG00000103696	Gm37531	11.8	12.04	6.474e-18	5.961e-17
ENSMUSG00000079658	Eloc	10.39	10.61	3.265e-18	3.531e-17
ENSMUSG00000018189	Uchl5	10.01	10.22	2.446e-18	2.899e-17
ENSMUSG00000101409	1700122D07Rik	10.04	10.94	5.042e-18	4.927e-17
ENSMUSG00000104418	Gm37070	10.33	10.35	3.666e-18	3.853e-17
ENSMUSG00000073494	Sh2d1b2	9.553	10.11	2.923e-18	3.256e-17
ENSMUSG00000026107	Nabp1	8.73	10.13	3.121e-18	3.443e-17
ENSMUSG00000072295	C2cd6	11.56	11.68	8.641e-18	7.511e-17
ENSMUSG00000012187	Mogat1	9.785	10.3	4.423e-18	4.438e-17
ENSMUSG00000042686	Jph1	11.18	13.54	1.557e-17	1.186e-16
ENSMUSG00000062963	Ufc1	12.47	13.63	1.646e-17	1.23e-16
ENSMUSG00000077257	Gm26080	10.11	9.99	3.926e-18	4.088e-17
ENSMUSG00000102784	Gm38251	9.294	10.03	4.28e-18	4.335e-17
ENSMUSG00000026182	Tnp1	10.24	10.82	8.108e-18	7.112e-17
ENSMUSG00000103636	Gm38331	10.24	10.28	5.66e-18	5.389e-17
ENSMUSG00000048874	Phf3	10.66	10.69	7.68e-18	6.79e-17
ENSMUSG00000026177	Slc11a1	10.33	10.99	9.263e-18	7.809e-17
ENSMUSG00000026258	3110079O15Rik	10.36	10.37	6.347e-18	5.91e-17
ENSMUSG00000042229	Rabif	9.478	10.95	9.37e-18	7.809e-17
ENSMUSG00000025940	Tmem70	9.437	10.29	6.354e-18	5.91e-17
ENSMUSG00000089803	Gm10171	9.856	9.885	4.196e-18	4.318e-17
ENSMUSG00000086053	Gm15178	9.246	9.745	3.568e-18	3.785e-17
ENSMUSG00000099719	Gm28802	9.188	9.713	3.467e-18	3.714e-17
ENSMUSG00000099760	Gm28800	10.69	10.37	7.343e-18	6.544e-17
ENSMUSG00000104368	2010103J01Rik	10.04	9.807	4.225e-18	4.318e-17
ENSMUSG00000046062	Ppp1r15b	10.66	10.23	6.807e-18	6.216e-17
ENSMUSG00000102380	Gm38140	9.725	10.56	9.149e-18	7.78e-17
ENSMUSG00000103497	Gm37407	11.36	11.67	1.53e-17	1.186e-16
ENSMUSG00000040485	Lrrc52	9.486	9.784	4.569e-18	4.544e-17
ENSMUSG00000103569	Gm38067	10.48	10.4	9.793e-18	8.025e-17
ENSMUSG00000005886	Ncoa2	10.51	10.01	7.087e-18	6.419e-17
ENSMUSG00000088951	Gm24940	8.742	10.19	8.711e-18	7.511e-17
ENSMUSG00000103804	Gm37062	9.351	9.664	4.835e-18	4.766e-17
ENSMUSG00000103180	Gm37089	10.96	11.11	1.559e-17	1.186e-16
ENSMUSG00000025938	Slco5a1	8.16	9.687	5.233e-18	5.07e-17
ENSMUSG00000048000	Gigyf2	10.15	10.57	1.259e-17	9.946e-17
ENSMUSG00000101911	Gm28381	9.477	9.657	5.544e-18	5.324e-17
ENSMUSG00000102234	Gm37885	9.785	9.702	6.366e-18	5.91e-17
ENSMUSG00000033845	Mrpl15	9.405	10.39	1.237e-17	9.84e-17
ENSMUSG00000026213	Stk11ip	9.938	11.11	1.923e-17	1.387e-16
ENSMUSG00000103159	F830112A20Rik	10.58	10.55	1.565e-17	1.186e-16
ENSMUSG00000046057	Gm15428	9.937	9.773	8.765e-18	7.511e-17
ENSMUSG00000102474	2610012C04Rik	10.77	11.83	2.845e-17	1.883e-16
ENSMUSG00000103760	Gm8009	11.16	10.26	1.542e-17	1.186e-16
ENSMUSG00000100556	Gm28902	10.89	11.21	2.523e-17	1.746e-16
ENSMUSG00000066595	Flvcr1	9.612	9.776	1.037e-17	8.428e-17
ENSMUSG00000025979	Mob4	11.35	11.95	3.313e-17	2.074e-16
ENSMUSG00000045210	Vcpip1	9.961	9.629	9.393e-18	7.809e-17
ENSMUSG00000026343	Gpr39	10.03	9.585	9.797e-18	8.025e-17
ENSMUSG00000026500	Cox20	9.668	10.27	1.93e-17	1.387e-16
ENSMUSG00000026112	Coa5	10.7	9.643	1.092e-17	8.815e-17
ENSMUSG00000026413	Pkp1	9.7	11.07	3.093e-17	1.958e-16
ENSMUSG00000067106	Gm5529	9.51	9.298	7.145e-18	6.419e-17
ENSMUSG00000103222	Gm37729	10.54	10.83	2.994e-17	1.949e-16
ENSMUSG00000103197	Gm37642	9.652	10.35	2.382e-17	1.669e-16
ENSMUSG00000033544	Angptl1	10.39	10.49	2.711e-17	1.837e-16
ENSMUSG00000026100	Mstn	8.781	9.917	1.969e-17	1.406e-16
ENSMUSG00000100389	Rbm6-ps2	9.186	9.376	1.16e-17	9.295e-17
ENSMUSG00000010290	AI597479	9.764	9.565	1.583e-17	1.191e-16
ENSMUSG00000103979	Gm20305	9.292	9.669	1.798e-17	1.309e-16
ENSMUSG00000026435	Slc45a3	10.45	10.42	3.563e-17	2.169e-16
ENSMUSG00000103215	Gm38388	9.857	9.985	2.694e-17	1.837e-16
ENSMUSG00000104524	Gm37333	10.92	10.27	3.645e-17	2.177e-16
ENSMUSG00000094131	Gm22265	9.131	9.409	1.712e-17	1.263e-16
ENSMUSG00000093650	Gm20631	9.723	9.861	3.009e-17	1.949e-16
ENSMUSG00000096297	Gm23934	10.78	10.67	4.98e-17	2.802e-16
ENSMUSG00000081429	Rps27-ps1	9.878	9.721	2.771e-17	1.849e-16
ENSMUSG00000041075	Fzd7	10.68	9.766	2.906e-17	1.905e-16
ENSMUSG00000026490	Cdc42bpa	9.994	10.09	3.802e-17	2.241e-16
ENSMUSG00000089822	Gm15759	8.938	9.907	3.383e-17	2.105e-16
ENSMUSG00000094245	Gm23966	11.09	10.93	5.829e-17	3.23e-16
ENSMUSG00000077220	Snord78	9.704	9.86	3.452e-17	2.136e-16
ENSMUSG00000096117	Gm26271	9.194	9.234	1.71e-17	1.263e-16
ENSMUSG00000044340	Phlpp1	9.325	9.405	2.202e-17	1.552e-16
ENSMUSG00000102558	Gm37571	9.355	9.629	2.857e-17	1.883e-16
ENSMUSG00000110596	Gm45859	9.534	9.397	2.171e-17	1.54e-16
ENSMUSG00000102157	Gm37470	9.199	9.693	3.138e-17	1.975e-16
ENSMUSG00000099519	Gm29253	9.249	9.531	2.732e-17	1.837e-16
ENSMUSG00000036907	C1ql2	7.741	11.58	8.162e-17	4.229e-16
ENSMUSG00000073485	H3f3aos	9.198	9.043	1.39e-17	1.091e-16
ENSMUSG00000026782	Abi2	9.754	9.589	3.082e-17	1.958e-16
ENSMUSG00000044689	Gm13749	8.761	9.125	1.784e-17	1.308e-16
ENSMUSG00000026153	Fam135a	9.669	9.478	3.054e-17	1.958e-16
ENSMUSG00000099959	Gm29487	10.51	9.352	2.66e-17	1.829e-16
ENSMUSG00000100319	Gm5256	9.046	9.595	3.589e-17	2.173e-16
ENSMUSG00000048234	Rnf149	9.075	9.463	3.075e-17	1.958e-16
ENSMUSG00000038855	Itpkb	11.49	13.76	1.216e-16	5.765e-16
ENSMUSG00000099753	Gm28058	8.525	9.24	2.399e-17	1.67e-16
ENSMUSG00000090322	Gm17090	9.707	10.18	6.091e-17	3.342e-16
ENSMUSG00000025991	Cps1	9.023	16.75	1.421e-16	6.461e-16
ENSMUSG00000070738	Dgkd	9.457	9.281	2.737e-17	1.837e-16
ENSMUSG00000034343	Ube2f	10.17	11.28	9.494e-17	4.829e-16
ENSMUSG00000048865	Arhgap30	9.793	9.968	5.61e-17	3.125e-16
ENSMUSG00000103226	Gm37732	8.842	9.61	4.179e-17	2.412e-16
ENSMUSG00000101746	2310043L19Rik	11.95	11.4	1.009e-16	5.039e-16
ENSMUSG00000025937	Lactb2	8.637	9.483	3.835e-17	2.248e-16
ENSMUSG00000026437	Cdk18	9.411	9.617	4.729e-17	2.688e-16
ENSMUSG00000077616	Gm25502	9.812	11.13	1.062e-16	5.233e-16
ENSMUSG00000038034	Igsf8	11.01	9.321	3.61e-17	2.174e-16
ENSMUSG00000101265	Gm29099	10.03	10.2	7.726e-17	4.041e-16
ENSMUSG00000103966	Gm37120	9.208	9.871	6.422e-17	3.473e-16
ENSMUSG00000100966	Gm5700	8.932	9.208	3.554e-17	2.169e-16
ENSMUSG00000025920	Stau2	10.83	10.94	1.138e-16	5.488e-16
ENSMUSG00000044306	4930500M09Rik	8.8	9.943	7.656e-17	4.039e-16
ENSMUSG00000049690	Nckap5	8.533	9.72	6.519e-17	3.508e-16
ENSMUSG00000026020	Nop58	9.142	9.22	3.766e-17	2.232e-16
ENSMUSG00000051081	Gm4847	10.09	9.426	5.064e-17	2.835e-16
ENSMUSG00000053318	Slamf8	8.149	9.238	4.148e-17	2.407e-16
ENSMUSG00000104237	Gm33533	11.31	11.97	1.553e-16	6.977e-16
ENSMUSG00000104212	Gm37986	9.281	9.083	3.654e-17	2.177e-16
ENSMUSG00000034107	Ano7	9.874	9.27	4.698e-17	2.684e-16
ENSMUSG00000073609	D2hgdh	9.49	9.123	4.476e-17	2.57e-16
ENSMUSG00000026514	Cnih3	9.465	9.888	9.942e-17	5.011e-16
ENSMUSG00000088911	Gm22609	9.371	8.995	3.943e-17	2.3e-16
ENSMUSG00000070182	Gm24817	7.739	9.325	6.256e-17	3.412e-16
ENSMUSG00000046367	Mgat4e	9.096	9.084	4.825e-17	2.729e-16
ENSMUSG00000104210	Gm37984	10.11	9.47	7.687e-17	4.039e-16
ENSMUSG00000104341	Gm37688	8.984	9.382	7.12e-17	3.795e-16
ENSMUSG00000103182	Gm37091	9.015	9.808	1.039e-16	5.167e-16
ENSMUSG00000097899	Gm16894	10.33	9.584	8.911e-17	4.575e-16
ENSMUSG00000067071	Hes6	7.155	12.19	2.147e-16	9.128e-16
ENSMUSG00000037138	Aff3	9.071	9.217	6.279e-17	3.412e-16
ENSMUSG00000104297	Gm38046	8.657	9.285	7.022e-17	3.761e-16
ENSMUSG00000103289	Gm31256	9.207	10.02	1.287e-16	6.049e-16
ENSMUSG00000026032	Ndufb3	8.688	8.794	3.511e-17	2.161e-16
ENSMUSG00000115958	Kiss1	9.761	9.688	1.125e-16	5.45e-16
ENSMUSG00000048174	Tmem81	11.17	10.59	1.746e-16	7.597e-16
ENSMUSG00000026491	Ahctf1	8.024	9.088	6.026e-17	3.323e-16
ENSMUSG00000104427	Gm38368	8.981	9.439	9.059e-17	4.629e-16
ENSMUSG00000099576	Gm29040	9.278	9.505	1.076e-16	5.282e-16
ENSMUSG00000028033	Kcnq5	8.759	10.33	1.809e-16	7.84e-16
ENSMUSG00000101275	1700012E03Rik	10.53	10.45	2.026e-16	8.714e-16
ENSMUSG00000103522	9430087J23Rik	10.03	10.46	2.136e-16	9.128e-16
ENSMUSG00000025932	Eya1	8.312	10.03	1.965e-16	8.484e-16
ENSMUSG00000100781	Gm29481	9.275	9.547	1.477e-16	6.662e-16
ENSMUSG00000026270	Capn10	8.638	8.985	8.505e-17	4.387e-16
ENSMUSG00000048775	Serpinb13	9.348	9.137	1.044e-16	5.168e-16
ENSMUSG00000026510	Trp53bp2	9.669	9.177	1.095e-16	5.348e-16
ENSMUSG00000103317	Gm38359	8.512	8.902	7.793e-17	4.057e-16
ENSMUSG00000052428	Tmco1	9.014	9.251	1.254e-16	5.919e-16
ENSMUSG00000033793	Atp6v1h	9.903	9.296	1.338e-16	6.186e-16
ENSMUSG00000026483	Fam129a	8.615	8.836	7.521e-17	3.99e-16
ENSMUSG00000006411	Nectin4	8.898	9.174	1.155e-16	5.545e-16
ENSMUSG00000103383	Gm37754	12.99	11.59	3.402e-16	1.404e-15
ENSMUSG00000047528	Als2cr12	9.754	10.44	2.646e-16	1.117e-15
ENSMUSG00000097680	Gm26642	10.1	9.249	1.317e-16	6.16e-16
ENSMUSG00000089088	Gm24001	8.829	9.159	1.207e-16	5.748e-16
ENSMUSG00000057329	Bcl2	10.17	9.342	1.58e-16	7.067e-16
ENSMUSG00000100069	Gm6801	7.25	9.054	1.196e-16	5.719e-16
ENSMUSG00000061024	Rrs1	8.437	9.308	1.637e-16	7.293e-16
ENSMUSG00000082057	Gm15789	8.4	8.878	1.002e-16	5.027e-16
ENSMUSG00000102027	Gm28914	9.034	9.177	1.427e-16	6.464e-16
ENSMUSG00000026622	Nek2	11.25	10.51	3.521e-16	1.442e-15
ENSMUSG00000098259	Gm27616	9.398	8.767	9.805e-17	4.965e-16
ENSMUSG00000102722	Gm37935	9.399	8.97	1.321e-16	6.16e-16
ENSMUSG00000033227	Wnt6	9.354	9.183	1.686e-16	7.422e-16
ENSMUSG00000101130	Gm19637	9.044	8.803	1.121e-16	5.45e-16
ENSMUSG00000090277	Gm8338	10.57	9.777	2.835e-16	1.192e-15
ENSMUSG00000102383	Gm38142	8.842	9.134	1.738e-16	7.597e-16
ENSMUSG00000100981	Gm29072	8.801	9.074	1.644e-16	7.296e-16
ENSMUSG00000104360	Gm8762	9.12	8.855	1.328e-16	6.162e-16
ENSMUSG00000055067	Smyd3	8.556	8.872	1.356e-16	6.221e-16
ENSMUSG00000107722	2900060B14Rik	8.271	8.826	1.399e-16	6.386e-16
ENSMUSG00000098549	Gm5269	8.62	8.883	1.743e-16	7.597e-16
ENSMUSG00000103609	Gm37022	8.623	8.648	1.357e-16	6.221e-16
ENSMUSG00000101413	Gm29485	10.32	8.944	2.33e-16	9.87e-16
ENSMUSG00000026307	Scly	8.758	8.862	2.146e-16	9.128e-16
ENSMUSG00000084749	Gm25288	10.06	9.105	2.91e-16	1.219e-15
ENSMUSG00000101693	Gm19461	9.167	9.233	3.75e-16	1.519e-15
ENSMUSG00000006542	Prkag3	8.74	8.485	1.68e-16	7.422e-16
ENSMUSG00000103597	Gm19777	9.828	9.184	3.909e-16	1.566e-15
ENSMUSG00000058665	En1	7.301	9.235	4.089e-16	1.627e-15
ENSMUSG00000026442	Nfasc	9.665	8.988	3.459e-16	1.422e-15
ENSMUSG00000101189	1700029M03Rik	5.883	10.36	7.537e-16	2.827e-15
ENSMUSG00000103576	Gm38188	9.021	9.655	5.91e-16	2.278e-15
ENSMUSG00000053461	Hhipl2	8.932	8.839	3.315e-16	1.378e-15
ENSMUSG00000078190	Dnm3os	8.273	8.686	3.051e-16	1.273e-15
ENSMUSG00000089204	Mir1927	8.655	9.255	5.372e-16	2.093e-15
ENSMUSG00000010311	Optc	9.736	9.398	6.114e-16	2.349e-15
ENSMUSG00000100786	Gm28940	9.206	9.64	7.396e-16	2.793e-15
ENSMUSG00000082473	Kif22-ps	9.708	8.829	3.928e-16	1.568e-15
ENSMUSG00000103393	Gm38304	9.062	8.762	3.876e-16	1.564e-15
ENSMUSG00000013997	Nit1	9.452	9.981	8.835e-16	3.206e-15
ENSMUSG00000089299	Gm24637	9.005	9.044	5.297e-16	2.07e-15
ENSMUSG00000025911	Adhfe1	8.196	9.807	8.938e-16	3.233e-15
ENSMUSG00000026172	Bcs1l	8.691	8.608	3.583e-16	1.462e-15
ENSMUSG00000102752	Gm7694	10.99	9.627	8.482e-16	3.108e-15
ENSMUSG00000103400	Gm15853	6.778	10.98	1.239e-15	4.301e-15
ENSMUSG00000081130	Gm8217	9.105	8.895	5.263e-16	2.064e-15
ENSMUSG00000103467	Gm38245	8.518	9.477	8.558e-16	3.126e-15
ENSMUSG00000026360	Rgs2	9.672	9.558	8.969e-16	3.234e-15
ENSMUSG00000026283	Ing5	8.181	8.903	5.603e-16	2.167e-15
ENSMUSG00000102685	Gm37373	8.273	8.655	4.3e-16	1.705e-15
ENSMUSG00000109510	Gm42417	9.816	9.625	9.682e-16	3.446e-15
ENSMUSG00000106081	Gm47280	9.183	9.249	7.704e-16	2.862e-15
ENSMUSG00000102824	Pdcd5-ps	9.413	8.496	3.748e-16	1.519e-15
ENSMUSG00000038793	Lefty1	8.361	8.492	3.888e-16	1.564e-15
ENSMUSG00000087213	2810408I11Rik	9.515	9.032	6.975e-16	2.643e-15
ENSMUSG00000103867	9630010A21Rik	8.577	9.841	1.117e-15	3.924e-15
ENSMUSG00000007097	Atp1a2	10.11	15.69	1.892e-15	6.31e-15
ENSMUSG00000102690	Gm37779	9.204	8.259	3.333e-16	1.38e-15
ENSMUSG00000042451	Mybph	9.045	10.63	1.605e-15	5.452e-15
ENSMUSG00000103964	Gm8407	8.971	9.279	9.881e-16	3.506e-15
ENSMUSG00000033124	Atg9a	10.1	8.761	6.835e-16	2.599e-15
ENSMUSG00000042182	Bend6	9.195	8.571	5.558e-16	2.157e-15
ENSMUSG00000033813	Tcea1	8.346	8.369	4.403e-16	1.739e-15
ENSMUSG00000077846	Gm22232	9.451	8.932	8.835e-16	3.206e-15
ENSMUSG00000049160	Tex50	8.065	8.4	4.928e-16	1.94e-15
ENSMUSG00000103961	Gm36388	8.74	8.74	7.65e-16	2.86e-15
ENSMUSG00000026469	Xpr1	7.763	8.898	9.167e-16	3.294e-15
ENSMUSG00000040225	Prrc2c	8.236	8.693	7.881e-16	2.907e-15
ENSMUSG00000100913	Gm8392	7.942	8.464	6.163e-16	2.359e-15
ENSMUSG00000100120	Gm553	9.033	8.458	6.674e-16	2.546e-15
ENSMUSG00000026211	Obsl1	9.642	9.445	1.473e-15	5.033e-15
ENSMUSG00000100516	Gm29332	8.948	8.912	1.089e-15	3.839e-15
ENSMUSG00000025955	Akr1cl	9.351	8.534	7.734e-16	2.862e-15
ENSMUSG00000038599	Capn8	9.115	8.713	9.583e-16	3.422e-15
ENSMUSG00000049881	2810025M15Rik	9.177	8.344	7.43e-16	2.796e-15
ENSMUSG00000094668	Gm24871	8.654	8.338	7.734e-16	2.862e-15
ENSMUSG00000100108	Gm28100	10.63	8.909	1.376e-15	4.73e-15
ENSMUSG00000102009	4933400F21Rik	10.68	9.299	1.847e-15	6.177e-15
ENSMUSG00000033276	Stk36	9.236	8.736	1.218e-15	4.239e-15
ENSMUSG00000104163	Gm19057	8.769	8.588	1.061e-15	3.753e-15
ENSMUSG00000098829	Mir6354	9.897	9.232	1.77e-15	5.938e-15
ENSMUSG00000067158	Col4a4	8.006	8.318	7.957e-16	2.925e-15
ENSMUSG00000089093	Snord11	9.082	8.782	1.315e-15	4.55e-15
ENSMUSG00000102858	Gm37086	9.677	9.74	2.364e-15	7.769e-15
ENSMUSG00000103403	Gm37931	10.6	9.485	2.451e-15	7.998e-15
ENSMUSG00000101414	Gm29101	8.864	8.686	1.634e-15	5.532e-15
ENSMUSG00000013973	Dedd	8.918	8.7	1.728e-15	5.833e-15
ENSMUSG00000110712	Gm39662	8.558	9.466	2.792e-15	8.964e-15
ENSMUSG00000087233	Gm43213	8.35	8.108	9.584e-16	3.422e-15
ENSMUSG00000104011	Gm32391	8.106	8.249	1.137e-15	3.984e-15
ENSMUSG00000026051	1500015O10Rik	8.186	8.464	1.526e-15	5.199e-15
ENSMUSG00000026564	Dusp27	8.885	8.236	1.213e-15	4.234e-15
ENSMUSG00000090165	Ugt1a10	8.827	8.73	2.064e-15	6.842e-15
ENSMUSG00000042305	Tmem183a	6.858	12.22	4.96e-15	1.536e-14
ENSMUSG00000089683	4930570N18Rik	8.26	8.16	1.349e-15	4.652e-15
ENSMUSG00000026095	Asnsd1	8.593	9.268	3.308e-15	1.053e-14
ENSMUSG00000103908	Gm37502	8.515	8.928	2.776e-15	8.936e-15
ENSMUSG00000101085	Gm8531	8.447	8.154	1.383e-15	4.741e-15
ENSMUSG00000026019	Wdr12	9.444	8.48	2.35e-15	7.746e-15
ENSMUSG00000026135	Zfp142	8.579	8.143	1.767e-15	5.938e-15
ENSMUSG00000026547	Tagln2	8.383	8.181	1.9e-15	6.319e-15
ENSMUSG00000064246	Chil1	10.01	11.26	6.381e-15	1.932e-14
ENSMUSG00000100696	Gm8419	8.459	8.759	3.292e-15	1.051e-14
ENSMUSG00000093538	Gm20711	8.893	8.456	2.649e-15	8.553e-15
ENSMUSG00000103983	Gm20045	8.52	8.19	2.489e-15	8.083e-15
ENSMUSG00000026204	Ptprn	8.82	8.568	3.571e-15	1.13e-14
ENSMUSG00000100361	Gm7063	7.864	9.223	5.293e-15	1.624e-14
ENSMUSG00000052423	B4galt3	7.98	8.118	2.409e-15	7.894e-15
ENSMUSG00000026614	Slc30a10	8.592	8.07	2.455e-15	7.998e-15
ENSMUSG00000102594	Gm38381	7.276	10.15	7.633e-15	2.268e-14
ENSMUSG00000101112	Gm29185	12.97	13.61	9.174e-15	2.689e-14
ENSMUSG00000026155	Smap1	8.393	7.978	2.283e-15	7.548e-15
ENSMUSG00000081214	Rpl35a-ps2	9.502	8.997	5.851e-15	1.781e-14
ENSMUSG00000102782	Gm37625	7.351	8.481	4.247e-15	1.329e-14
ENSMUSG00000100280	Gm28417	8.535	8.258	3.532e-15	1.121e-14
ENSMUSG00000042197	Zfp451	7.4	8.107	3.126e-15	1.001e-14
ENSMUSG00000102689	Gm37217	10.82	11.1	9.755e-15	2.852e-14
ENSMUSG00000102049	Zbed6	8.018	8.4	4.176e-15	1.311e-14
ENSMUSG00000095731	Gm25312	9.155	8.661	5.191e-15	1.597e-14
ENSMUSG00000058248	Kcnh1	4.914	8.329	4.027e-15	1.267e-14
ENSMUSG00000047330	Kcne4	10.37	9.425	8.389e-15	2.486e-14
ENSMUSG00000026576	Atp1b1	8.433	8.407	4.949e-15	1.536e-14
ENSMUSG00000038776	Ephx1	7.941	7.777	2.579e-15	8.353e-15
ENSMUSG00000101964	Gm28693	7.706	8.329	5.068e-15	1.564e-14
ENSMUSG00000041879	Ipo9	8.724	8.494	5.794e-15	1.768e-14
ENSMUSG00000059956	Serpinb12	8.779	8.424	5.702e-15	1.745e-14
ENSMUSG00000053153	Spag16	7.639	8.182	4.962e-15	1.536e-14
ENSMUSG00000104509	Gm33994	7.211	7.92	3.936e-15	1.242e-14
ENSMUSG00000026170	Cyp27a1	7.817	8.449	6.757e-15	2.04e-14
ENSMUSG00000041945	Mfsd9	7.995	10.24	1.408e-14	4.075e-14
ENSMUSG00000100702	Gm8976	8.601	8.327	7.006e-15	2.103e-14
ENSMUSG00000026278	Bok	7.704	8.278	7.024e-15	2.103e-14
ENSMUSG00000025935	Tram1	8.92	8.946	1.075e-14	3.128e-14
ENSMUSG00000093286	Gm22569	8.747	8.164	6.852e-15	2.063e-14
ENSMUSG00000026275	Ppp1r7	7.517	7.833	4.891e-15	1.526e-14
ENSMUSG00000026127	Imp4	7.795	8.194	7.224e-15	2.158e-14
ENSMUSG00000104087	Gm38277	6.797	9.179	1.482e-14	4.266e-14
ENSMUSG00000089706	B230216N24Rik	9.986	9.545	1.707e-14	4.888e-14
ENSMUSG00000102776	Gm38162	7.733	7.796	6.2e-15	1.882e-14
ENSMUSG00000103727	Gm37097	7.631	8.088	8.466e-15	2.502e-14
ENSMUSG00000070871	Ccnyl1	7.634	8.137	9.858e-15	2.875e-14
ENSMUSG00000053333	Dis3l2	7.983	7.981	8.805e-15	2.588e-14
ENSMUSG00000093956	Gm24497	8.618	8.944	1.712e-14	4.889e-14
ENSMUSG00000101395	Gm8241	8.129	7.91	8.746e-15	2.578e-14
ENSMUSG00000042554	Zp3r	7.834	9.37	2.041e-14	5.77e-14
ENSMUSG00000038936	Sccpdh	8.368	7.701	7.387e-15	2.2e-14
ENSMUSG00000099556	Gm28857	8.75	8.248	1.266e-14	3.672e-14
ENSMUSG00000098368	Gm27648	7.697	8.664	1.731e-14	4.932e-14
ENSMUSG00000102976	Zc3h11a	7.939	8.353	1.457e-14	4.206e-14
ENSMUSG00000040181	Fmo1	6.487	10.04	2.748e-14	7.615e-14
ENSMUSG00000101482	Gm28883	10.3	9.012	2.496e-14	7.003e-14
ENSMUSG00000103646	Gm37706	9.739	8.339	1.816e-14	5.16e-14
ENSMUSG00000098680	Mir6898	8.081	8.663	2.189e-14	6.173e-14
ENSMUSG00000090399	Gm38399	9.554	8.835	2.53e-14	7.081e-14
ENSMUSG00000102596	Gm20203	8.256	8.051	1.641e-14	4.712e-14
ENSMUSG00000073491	Ifi213	11.25	8.749	2.618e-14	7.292e-14
ENSMUSG00000100106	Gm28856	8.389	8.136	1.892e-14	5.364e-14
ENSMUSG00000082528	Gm15182	8.788	8.846	2.956e-14	8.036e-14
ENSMUSG00000061863	Gm6822	8.563	9.285	3.542e-14	9.44e-14
ENSMUSG00000100351	Gm7867	8.847	8.591	3.052e-14	8.256e-14
ENSMUSG00000071890	Mroh9	8.089	9.851	4.352e-14	1.143e-13
ENSMUSG00000047443	Erfe	8.109	8.009	2.388e-14	6.717e-14
ENSMUSG00000026202	Tuba4a	8.597	8.231	3.062e-14	8.26e-14
ENSMUSG00000116158	Kiss1	10.08	8.378	3.338e-14	8.918e-14
ENSMUSG00000103328	Gm37406	8.556	8.298	3.26e-14	8.729e-14
ENSMUSG00000100504	Gm28926	9.008	9.345	4.897e-14	1.277e-13
ENSMUSG00000082160	Gm11578	9.536	8.099	3.124e-14	8.385e-14
ENSMUSG00000051079	Rgs13	9.233	7.949	2.835e-14	7.799e-14
ENSMUSG00000104140	Gm37140	8.371	8.067	3.053e-14	8.256e-14
ENSMUSG00000101741	Gm28055	8.112	9.173	5.12e-14	1.333e-13
ENSMUSG00000038174	Fam126b	8.113	7.844	2.814e-14	7.77e-14
ENSMUSG00000103146	Gm37745	7.482	7.795	2.712e-14	7.534e-14
ENSMUSG00000025939	Ube2w	8.41	7.85	2.958e-14	8.036e-14
ENSMUSG00000099996	Gm29065	9.504	7.779	2.908e-14	7.96e-14
ENSMUSG00000090550	Prlh	8.334	10.2	6.396e-14	1.645e-13
ENSMUSG00000104026	Gm37212	9.521	8.032	3.733e-14	9.925e-14
ENSMUSG00000042207	Kdm5b	8.136	7.688	2.871e-14	7.877e-14
ENSMUSG00000089906	Gm6428	8.075	7.744	3.085e-14	8.301e-14
ENSMUSG00000100685	Gm28610	7.442	8.723	5.382e-14	1.394e-13
ENSMUSG00000026581	Sell	8.314	8.059	4.055e-14	1.073e-13
ENSMUSG00000026463	Atp2b4	7.469	7.521	2.568e-14	7.17e-14
ENSMUSG00000097540	1700037F24Rik	8.75	8.165	4.418e-14	1.158e-13
ENSMUSG00000097784	A430105J06Rik	8.407	9.225	6.676e-14	1.706e-13
ENSMUSG00000086721	Gm13750	8.259	7.443	2.818e-14	7.77e-14
ENSMUSG00000101731	3830432H09Rik	7.812	7.466	2.937e-14	8.02e-14
ENSMUSG00000026090	2010300C02Rik	8.575	7.733	3.958e-14	1.05e-13
ENSMUSG00000101660	Gm18666	7.781	7.872	4.661e-14	1.219e-13
ENSMUSG00000034292	Traf3ip1	9.439	9.084	9.591e-14	2.385e-13
ENSMUSG00000090682	Gm3852	7.829	9.263	1.028e-13	2.533e-13
ENSMUSG00000101656	Gm8004	7.799	7.634	5.26e-14	1.366e-13
ENSMUSG00000025902	Sox17	7.208	7.839	6.452e-14	1.656e-13
ENSMUSG00000091017	Fam71a	7.285	7.317	4.23e-14	1.117e-13
ENSMUSG00000026389	Steap3	4.58	8.155	8.068e-14	2.038e-13
ENSMUSG00000046337	Fam178b	7.425	7.322	4.304e-14	1.134e-13
ENSMUSG00000103670	Gm37622	8.182	8.734	1.022e-13	2.53e-13
ENSMUSG00000104461	Gm37662	8.036	8.937	1.089e-13	2.679e-13
ENSMUSG00000077608	Gm22671	8.952	10.44	1.301e-13	3.144e-13
ENSMUSG00000103123	Gm37390	7.939	7.631	6.095e-14	1.575e-13
ENSMUSG00000103238	Gm36527	8.021	8.065	8.237e-14	2.076e-13
ENSMUSG00000102717	Gm37759	6.775	9.288	1.226e-13	2.982e-13
ENSMUSG00000093003	Mir3473c	8.471	8.36	1.002e-13	2.486e-13
ENSMUSG00000090533	Gm8214	7.655	7.567	6.579e-14	1.685e-13
ENSMUSG00000099648	Gm5975	7.963	7.704	7.376e-14	1.876e-13
ENSMUSG00000033007	Asic4	5.604	7.875	8.38e-14	2.107e-13
ENSMUSG00000008475	Arpc5	8.662	8.105	9.565e-14	2.384e-13
ENSMUSG00000041779	Tram2	6.528	7.662	7.747e-14	1.966e-13
ENSMUSG00000001138	Cnnm3	7.977	7.463	6.393e-14	1.645e-13
ENSMUSG00000059647	Cbx3-ps7	9.824	8.485	1.177e-13	2.882e-13
ENSMUSG00000050777	Tmem37	7.425	7.814	8.973e-14	2.251e-13
ENSMUSG00000048960	Prex2	9.349	8.314	1.207e-13	2.943e-13
ENSMUSG00000081562	Gm11575	7.415	7.499	7.833e-14	1.983e-13
ENSMUSG00000047539	Fbxo28	8.4	8.536	1.383e-13	3.313e-13
ENSMUSG00000088562	Gm23780	5.473	7.69	9.284e-14	2.324e-13
ENSMUSG00000099913	Gm28551	7.045	7.334	7.216e-14	1.839e-13
ENSMUSG00000095238	Gm4845	9.116	9.33	1.783e-13	4.234e-13
ENSMUSG00000102211	Gm37490	7.888	7.925	1.191e-13	2.91e-13
ENSMUSG00000025915	Sgk3	8.217	7.987	1.269e-13	3.074e-13
ENSMUSG00000082745	Gm15181	8.914	7.423	9.541e-14	2.383e-13
ENSMUSG00000099624	Gm7889	6.993	7.713	1.346e-13	3.245e-13
ENSMUSG00000104476	Gm38211	8.995	11.43	2.381e-13	5.526e-13
ENSMUSG00000084468	Gm22955	9.189	8.378	1.869e-13	4.422e-13
ENSMUSG00000043629	1700019D03Rik	8.757	7.684	1.349e-13	3.246e-13
ENSMUSG00000102687	Gm37219	7.401	7.316	1.024e-13	2.531e-13
ENSMUSG00000079045	Prox1os	7.071	7.4	1.141e-13	2.8e-13
ENSMUSG00000026199	Ankzf1	7.503	7.632	1.36e-13	3.266e-13
ENSMUSG00000026457	Adipor1	4.291	7.832	1.606e-13	3.823e-13
ENSMUSG00000089703	Gm15833	7.996	7.597	1.45e-13	3.466e-13
ENSMUSG00000060519	Tor3a	8.126	7.232	1.24e-13	3.01e-13
ENSMUSG00000102648	Gm38022	6.44	7.742	1.974e-13	4.629e-13
ENSMUSG00000016918	Sulf1	8.57	8.474	2.657e-13	6.128e-13
ENSMUSG00000087747	Gm25151	8.19	7.936	2.269e-13	5.278e-13
ENSMUSG00000085842	Pard3bos1	6.67	7.403	1.925e-13	4.524e-13
ENSMUSG00000080950	Gm7278	7.349	7.204	1.593e-13	3.8e-13
ENSMUSG00000049439	Cyp20a1	7.663	7.226	1.849e-13	4.382e-13
ENSMUSG00000065516	Mir214	10.28	8.018	3.071e-13	7.025e-13
ENSMUSG00000007805	Twist2	7.915	7.34	2.16e-13	5.044e-13
ENSMUSG00000026154	Sdhaf4	6.841	7.171	1.899e-13	4.482e-13
ENSMUSG00000039224	D1Pas1	7.063	7.586	2.694e-13	6.202e-13
ENSMUSG00000087949	Gm24251	8.314	8.097	3.502e-13	7.977e-13
ENSMUSG00000026200	Glb1l	6.497	7.161	2.138e-13	5.004e-13
ENSMUSG00000049528	Olfr429	4.318	7.888	3.764e-13	8.522e-13
ENSMUSG00000103751	Gm37283	5.32	6.932	1.911e-13	4.502e-13
ENSMUSG00000102961	Gm19918	6.635	7.222	2.474e-13	5.731e-13
ENSMUSG00000094651	Gal3st2	8.124	7.596	3.518e-13	7.998e-13
ENSMUSG00000085054	Gm15834	7.118	7.156	2.601e-13	6.012e-13
ENSMUSG00000103415	Gm37339	6.831	6.927	2.226e-13	5.189e-13
ENSMUSG00000094013	Gm25829	8.63	7.132	2.794e-13	6.417e-13
ENSMUSG00000026102	Inpp1	9.496	8.179	4.823e-13	1.083e-12
ENSMUSG00000085812	Gm15843	6.718	7.869	4.294e-13	9.663e-13
ENSMUSG00000064816	Gm22357	7.872	7.245	3.332e-13	7.607e-13
ENSMUSG00000026167	Wnt10a	8.362	11.05	6.467e-13	1.432e-12
ENSMUSG00000086264	Gm15850	8.528	7.528	4.072e-13	9.183e-13
ENSMUSG00000104827	Gm24826	9.117	9.163	6.382e-13	1.419e-12
ENSMUSG00000100160	Gm28527	9.191	7.821	5.061e-13	1.134e-12
ENSMUSG00000026049	Tex30	5.3	8.903	6.621e-13	1.458e-12
ENSMUSG00000101674	4930444A19Rik	8.181	6.985	3.066e-13	7.025e-13
ENSMUSG00000056055	Sag	6.93	7.028	3.716e-13	8.432e-13
ENSMUSG00000098234	Snhg6	6.422	7.122	4.014e-13	9.069e-13
ENSMUSG00000094834	Gm25472	6.429	7.382	5.441e-13	1.217e-12
ENSMUSG00000050967	Creg2	7.772	7.553	6.396e-13	1.419e-12
ENSMUSG00000099950	9130227L01Rik	9.009	8.161	7.916e-13	1.722e-12
ENSMUSG00000103230	Gm17970	7.432	7.708	6.934e-13	1.524e-12
ENSMUSG00000038209	Itln1	7.349	7.894	7.991e-13	1.732e-12
ENSMUSG00000101574	1700112H15Rik	8.625	8.908	9.891e-13	2.131e-12
ENSMUSG00000061584	Lyg2	6.407	7.159	5.777e-13	1.29e-12
ENSMUSG00000059498	Fcgr3	6.026	7.311	6.591e-13	1.454e-12
ENSMUSG00000056763	Cspp1	8.298	7.42	7.272e-13	1.592e-12
ENSMUSG00000097264	Rpl31-ps24	6.987	7.433	7.936e-13	1.723e-12
ENSMUSG00000100736	Gm5147	7.632	7.406	7.916e-13	1.722e-12
ENSMUSG00000104482	Gm38064	9.555	8.437	1.116e-12	2.4e-12
ENSMUSG00000103072	Gm37905	8.011	6.971	6.493e-13	1.435e-12
ENSMUSG00000099383	Gm28925	9.47	8.688	1.268e-12	2.711e-12
ENSMUSG00000095955	Gm22360	7.163	6.78	5.943e-13	1.324e-12
ENSMUSG00000089275	Gm24548	7.532	7.206	8.365e-13	1.806e-12
ENSMUSG00000025921	Rdh10	7.267	6.854	7.565e-13	1.652e-12
ENSMUSG00000096172	Gm22992	7.269	6.619	7.168e-13	1.572e-12
ENSMUSG00000026088	Mitd1	7.523	7.402	1.235e-12	2.645e-12
ENSMUSG00000102642	A130048G24Rik	7.764	7.517	1.358e-12	2.899e-12
ENSMUSG00000016194	Hsd11b1	6.316	6.617	8.096e-13	1.751e-12
ENSMUSG00000050229	Pigm	9.526	8.047	1.753e-12	3.72e-12
ENSMUSG00000103379	Gm37177	11.53	11.32	2.257e-12	4.761e-12
ENSMUSG00000099553	Gm29538	8.286	7.418	1.798e-12	3.809e-12
ENSMUSG00000046828	Mettl21e	6.847	6.703	1.227e-12	2.633e-12
ENSMUSG00000040865	Ino80d	8.307	8.12	2.57e-12	5.391e-12
ENSMUSG00000050526	4933406M09Rik	9.666	7.48	2.338e-12	4.923e-12
ENSMUSG00000026358	Rgs1	7.123	6.829	1.734e-12	3.686e-12
ENSMUSG00000041859	Mcm3	5.889	6.789	1.906e-12	4.028e-12
ENSMUSG00000089782	Gm3531	6.548	6.29	1.605e-12	3.419e-12
ENSMUSG00000009905	Kdsr	7.608	6.983	2.788e-12	5.826e-12
ENSMUSG00000098509	Rgs21	7.989	7.899	3.914e-12	8.059e-12
ENSMUSG00000026473	Glul	5.541	7.4	3.512e-12	7.272e-12
ENSMUSG00000038026	Kcnj9	5.275	8.311	4.814e-12	9.877e-12
ENSMUSG00000103640	Gm31406	6.943	6.41	2.511e-12	5.279e-12
ENSMUSG00000101958	Gm19085	7.016	6.655	2.972e-12	6.188e-12
ENSMUSG00000016493	Cd46	9.581	8.052	4.914e-12	1.006e-11
ENSMUSG00000041396	Mettl18	4.672	7.921	4.967e-12	1.015e-11
ENSMUSG00000106307	Gm23370	8.785	6.414	2.815e-12	5.872e-12
ENSMUSG00000104386	Gm37030	6.663	6.323	2.661e-12	5.572e-12
ENSMUSG00000016481	Cr1l	6.91	6.344	3.098e-12	6.439e-12
ENSMUSG00000056268	Dennd1b	5.918	6.441	3.501e-12	7.262e-12
ENSMUSG00000100020	Gm19086	6.717	6.398	3.553e-12	7.329e-12
ENSMUSG00000091806	Gm5258	7.223	6.208	3.539e-12	7.315e-12
ENSMUSG00000048096	Lmod1	5.937	6.308	4.392e-12	9.028e-12
ENSMUSG00000026171	Rnf25	9.159	9.643	8.21e-12	1.652e-11
ENSMUSG00000006576	Slc4a3	7.585	6.673	6.205e-12	1.264e-11
ENSMUSG00000103686	Gm37073	8.165	7.569	8.746e-12	1.755e-11
ENSMUSG00000101701	4930521E06Rik	6.179	6.454	5.96e-12	1.216e-11
ENSMUSG00000103156	Gm38293	6.941	7.182	8.217e-12	1.652e-11
ENSMUSG00000102433	Gm38050	5.422	8.681	1.032e-11	2.046e-11
ENSMUSG00000026158	Ogfrl1	9.793	8.281	1.042e-11	2.058e-11
ENSMUSG00000009418	Nav1	6.329	8.547	1.08e-11	2.13e-11
ENSMUSG00000103501	Gm7412	6.672	6.584	7.041e-12	1.424e-11
ENSMUSG00000058076	Sdhc	6.191	6.525	7.018e-12	1.421e-11
ENSMUSG00000103006	4933417C20Rik	6.902	6.99	8.761e-12	1.755e-11
ENSMUSG00000055567	Unc80	10.06	6.569	7.291e-12	1.471e-11
ENSMUSG00000026245	Farsb	6.325	6.301	6.385e-12	1.298e-11
ENSMUSG00000042708	Shcbp1l	6.666	7.427	1.003e-11	1.992e-11
ENSMUSG00000026027	Stradb	5.733	6.93	9.294e-12	1.855e-11
ENSMUSG00000084416	Rpl10a-ps1	6.07	7.048	9.777e-12	1.945e-11
ENSMUSG00000089169	Gm25413	8.17	6.851	1.04e-11	2.058e-11
ENSMUSG00000091993	B930036N10Rik	5.594	5.952	6.764e-12	1.372e-11
ENSMUSG00000014602	Kif1a	7.431	6.544	9.507e-12	1.895e-11
ENSMUSG00000089700	Gm2343	6.838	6.229	9.044e-12	1.809e-11
ENSMUSG00000083066	Gm12748	9.513	6.805	1.201e-11	2.359e-11
ENSMUSG00000100679	Gm28778	6.584	6.031	1.173e-11	2.31e-11
ENSMUSG00000100040	Gm28467	7.581	10.1	2.083e-11	4.056e-11
ENSMUSG00000100215	Gm8292	7.524	6.308	1.791e-11	3.506e-11
ENSMUSG00000053483	Usp21	6.07	6.583	2.032e-11	3.971e-11
ENSMUSG00000026289	Atg16l1	5.258	7.557	2.562e-11	4.955e-11
ENSMUSG00000026197	Zfand2b	5.247	6.05	1.71e-11	3.355e-11
ENSMUSG00000096851	Gm22756	8.527	10.06	2.611e-11	5.041e-11
ENSMUSG00000098850	Gm27390	6.615	6.123	2.069e-11	4.037e-11
ENSMUSG00000026150	Mff	6.003	6.412	2.492e-11	4.828e-11
ENSMUSG00000026694	Mettl13	4.807	6.029	2.173e-11	4.224e-11
ENSMUSG00000090031	4732440D04Rik	6.445	5.997	2.376e-11	4.612e-11
ENSMUSG00000026536	Mnda	5.044	7.187	3.719e-11	7.144e-11
ENSMUSG00000026433	Rab29	5.021	6.417	3.354e-11	6.464e-11
ENSMUSG00000016179	Camk1g	8.193	7.705	4.417e-11	8.397e-11
ENSMUSG00000036766	Dner	7.799	6.813	3.982e-11	7.609e-11
ENSMUSG00000099938	C030007H22Rik	7.535	7.244	4.365e-11	8.312e-11
ENSMUSG00000087131	Gm16152	8.314	6.99	4.235e-11	8.079e-11
ENSMUSG00000025925	Terf1	7.102	6.671	3.892e-11	7.449e-11
ENSMUSG00000090081	Gm16587	5.609	6.137	3.621e-11	6.966e-11
ENSMUSG00000103325	Gm10531	5.243	6.853	5.718e-11	1.081e-10
ENSMUSG00000101328	Rpl30-ps6	5.327	5.644	3.868e-11	7.416e-11
ENSMUSG00000026659	Dusp12	6.029	6.481	5.675e-11	1.075e-10
ENSMUSG00000103975	Gm37676	5.733	6.109	5.526e-11	1.049e-10
ENSMUSG00000102217	Gm37491	6.185	6.054	5.735e-11	1.083e-10
ENSMUSG00000104251	Gm18036	6.354	6.224	6.537e-11	1.23e-10
ENSMUSG00000099563	Gm19125	6.771	5.988	6.043e-11	1.139e-10
ENSMUSG00000088779	Gm26034	6.181	6.644	9.214e-11	1.725e-10
ENSMUSG00000046836	Brox	4.833	6.52	9.167e-11	1.719e-10
ENSMUSG00000088353	Gm24014	7.04	6.238	8.734e-11	1.641e-10
ENSMUSG00000084989	Crocc2	10.92	7.339	1.065e-10	1.975e-10
ENSMUSG00000026070	Il18r1	5.127	6.671	1.012e-10	1.889e-10
ENSMUSG00000104158	Gm38100	7.099	6.069	1.029e-10	1.913e-10
ENSMUSG00000101826	Gm8581	6.118	5.645	9.529e-11	1.781e-10
ENSMUSG00000064954	Gm24919	6.094	5.986	1.055e-10	1.959e-10
ENSMUSG00000099949	Gm29124	8.009	6.816	1.334e-10	2.461e-10
ENSMUSG00000026675	Hsd17b7	8.649	7.765	1.451e-10	2.671e-10
ENSMUSG00000041684	Bivm	5.519	5.592	1.028e-10	1.913e-10
ENSMUSG00000104989	Gm44481	8.535	6.935	1.477e-10	2.715e-10
ENSMUSG00000100302	Gm29670	6.479	5.545	1.164e-10	2.154e-10
ENSMUSG00000102446	Gm17965	8.132	6.661	1.632e-10	2.99e-10
ENSMUSG00000096094	A630095N17Rik	6.211	5.334	1.242e-10	2.294e-10
ENSMUSG00000013593	Ndufs2	9.249	7.995	2.048e-10	3.734e-10
ENSMUSG00000026426	Arl8a	4.742	6.157	1.805e-10	3.302e-10
ENSMUSG00000099868	Gm28792	6.196	5.716	1.63e-10	2.99e-10
ENSMUSG00000103517	Gm37432	6.385	5.559	1.941e-10	3.544e-10
ENSMUSG00000033364	Usp37	5.367	6.124	2.369e-10	4.313e-10
ENSMUSG00000025777	Gdap1	9.828	8.537	2.661e-10	4.827e-10
ENSMUSG00000047048	Olfr432	6.191	6.913	2.699e-10	4.888e-10
ENSMUSG00000101583	Gm20753	4.731	5.653	2.528e-10	4.595e-10
ENSMUSG00000045259	Klhdc9	6.584	5.818	2.989e-10	5.406e-10
ENSMUSG00000100457	D830032E09Rik	5.26	5.909	3.084e-10	5.567e-10
ENSMUSG00000039318	Rab3gap2	8.136	6.121	3.543e-10	6.387e-10
ENSMUSG00000102509	Gm37368	6.158	5.928	3.834e-10	6.888e-10
ENSMUSG00000102709	Gm38308	9.458	6.113	4.037e-10	7.242e-10
ENSMUSG00000102459	Gm38352	6.005	6.347	4.464e-10	7.982e-10
ENSMUSG00000033701	Acbd6	6.359	5.248	3.614e-10	6.505e-10
ENSMUSG00000089276	Gm24545	5.743	5.396	4.438e-10	7.949e-10
ENSMUSG00000079436	Kcnj13	7.695	6.661	5.907e-10	1.051e-09
ENSMUSG00000102559	Gm37570	9.556	5.941	5.374e-10	9.593e-10
ENSMUSG00000100357	Gm28181	6.002	5.884	5.697e-10	1.015e-09
ENSMUSG00000025776	Crispld1	5.345	5.626	6.707e-10	1.192e-09
ENSMUSG00000026443	Lrrn2	5.673	5.611	6.741e-10	1.196e-09
ENSMUSG00000026394	Atp6v1g3	5.723	5.997	7.391e-10	1.309e-09
ENSMUSG00000040918	Slc19a2	3.623	6.175	8.395e-10	1.48e-09
ENSMUSG00000099562	Gm19027	4.715	5.312	8.04e-10	1.42e-09
ENSMUSG00000087374	Gm15457	6.006	5.269	7.97e-10	1.409e-09
ENSMUSG00000026568	Mpc2	6.532	5.712	8.951e-10	1.575e-09
ENSMUSG00000073702	Rpl31	6.492	5.932	1.025e-09	1.795e-09
ENSMUSG00000026281	Dtymk	6.124	5.042	8.987e-10	1.579e-09
ENSMUSG00000102055	Gm28913	7.122	5.359	1.006e-09	1.764e-09
ENSMUSG00000063558	Aox1	6.418	5.305	1.244e-09	2.168e-09
ENSMUSG00000098923	Tmem185b	6.674	6.135	1.458e-09	2.526e-09
ENSMUSG00000079138	Gm8818	8.348	4.917	1.203e-09	2.1e-09
ENSMUSG00000102755	Gm37055	4.62	5.509	1.399e-09	2.431e-09
ENSMUSG00000102231	1700074A21Rik	4.65	5.591	1.429e-09	2.479e-09
ENSMUSG00000103771	Gm36934	9.537	10.3	1.201e-09	2.1e-09
ENSMUSG00000102714	Gm37618	5.799	4.807	1.378e-09	2.398e-09
ENSMUSG00000047384	A730013G03Rik	5.536	5.376	1.661e-09	2.874e-09
ENSMUSG00000101242	Gm28319	7.565	5.839	1.824e-09	3.146e-09
ENSMUSG00000025967	Eef1b2	5.546	4.902	1.727e-09	2.984e-09
ENSMUSG00000096474	Gm5561	8.5	5.924	2.072e-09	3.562e-09
ENSMUSG00000067780	Pi15	4.542	5.292	1.918e-09	3.302e-09
ENSMUSG00000101324	Gm29187	6.757	5.474	2.449e-09	4.203e-09
ENSMUSG00000065215	Gm26263	7.369	5.501	2.547e-09	4.365e-09
ENSMUSG00000026495	Efcab2	4.906	5.4	2.586e-09	4.418e-09
ENSMUSG00000103577	9330162B11Rik	5.41	5.031	2.57e-09	4.398e-09
ENSMUSG00000102941	Gm37552	8.042	6.031	2.931e-09	4.992e-09
ENSMUSG00000084093	Gm16418	7.627	6.563	3.16e-09	5.366e-09
ENSMUSG00000102733	Gm38262	5.199	5.241	2.935e-09	4.992e-09
ENSMUSG00000064414	n-R5s215	4.687	4.554	2.681e-09	4.574e-09
ENSMUSG00000101905	4930439D14Rik	7.528	5.581	3.436e-09	5.808e-09
ENSMUSG00000040782	Cop1	4.866	5.322	3.435e-09	5.808e-09
ENSMUSG00000025933	Tmem14a	6.044	4.616	3.255e-09	5.519e-09
ENSMUSG00000097281	Gm26685	5.837	5.078	3.556e-09	6.001e-09
ENSMUSG00000100767	Gm3508	6.299	4.896	3.647e-09	6.147e-09
ENSMUSG00000102909	Gm2453	7.967	6.458	4.309e-09	7.229e-09
ENSMUSG00000102789	Gm38256	5.104	6.663	4.416e-09	7.398e-09
ENSMUSG00000066652	Lefty2	4.425	5.477	4.195e-09	7.06e-09
ENSMUSG00000103087	Gm38122	6.706	5.622	4.266e-09	7.168e-09
ENSMUSG00000079137	Rpl27-ps1	7.215	5.45	4.865e-09	8.125e-09
ENSMUSG00000026447	Pik3c2b	7.763	4.851	4.596e-09	7.687e-09
ENSMUSG00000103401	Gm37929	5.313	5.358	5.403e-09	8.997e-09
ENSMUSG00000032487	Ptgs2	5.598	5.056	5.229e-09	8.72e-09
ENSMUSG00000039783	Kmo	6.786	5.785	5.645e-09	9.372e-09
ENSMUSG00000026277	Stk25	5.16	5.087	5.856e-09	9.708e-09
ENSMUSG00000039342	Ankar	6.831	4.926	5.971e-09	9.883e-09
ENSMUSG00000096310	Gm23523	7.054	4.716	6.38e-09	1.054e-08
ENSMUSG00000026411	Tmem9	4.381	5.197	7.381e-09	1.218e-08
ENSMUSG00000037499	Nenf	7.197	9.96	5.606e-09	9.322e-09
ENSMUSG00000060715	1700019A02Rik	6.41	5.406	7.99e-09	1.313e-08
ENSMUSG00000103380	Gm37756	6.594	4.671	7.846e-09	1.291e-08
ENSMUSG00000026156	B3gat2	6.417	4.616	7.833e-09	1.291e-08
ENSMUSG00000064936	Gm23722	4.923	4.602	8.172e-09	1.341e-08
ENSMUSG00000086726	Gm9458	3.824	4.504	8.539e-09	1.399e-08
ENSMUSG00000062580	Timm17a	8.847	5.446	9.406e-09	1.534e-08
ENSMUSG00000052477	C130026I21Rik	4.919	4.81	8.954e-09	1.465e-08
ENSMUSG00000102981	Gm38322	4.594	4.593	9.242e-09	1.51e-08
ENSMUSG00000100334	C230024C17Rik	5.198	5.864	1.14e-08	1.857e-08
ENSMUSG00000102718	Gm37761	4.485	4.904	1.163e-08	1.892e-08
ENSMUSG00000097760	6030442K20Rik	5.28	4.802	1.366e-08	2.219e-08
ENSMUSG00000103588	Gm18445	5.355	5.782	1.526e-08	2.474e-08
ENSMUSG00000045381	Olfr433	3.065	4.982	1.743e-08	2.823e-08
ENSMUSG00000042581	Thsd7b	4.706	4.828	2.022e-08	3.264e-08
ENSMUSG00000102280	Gm36999	4.435	4.012	2.019e-08	3.264e-08
ENSMUSG00000055866	Per2	2.735	4.757	2.185e-08	3.522e-08
ENSMUSG00000025931	Paqr8	9.067	5.969	2.516e-08	4.045e-08
ENSMUSG00000073730	4933415F23Rik	2.781	4.871	2.462e-08	3.963e-08
ENSMUSG00000101333	Gm4753	8.198	5.4	2.591e-08	4.16e-08
ENSMUSG00000026701	Prdx6	4.681	4.356	2.661e-08	4.265e-08
ENSMUSG00000086056	Gm4846	4.452	3.916	2.669e-08	4.271e-08
ENSMUSG00000026131	Dst	8.552	5.156	2.906e-08	4.638e-08
ENSMUSG00000101249	Gm29216	3.963	4.047	2.872e-08	4.59e-08
ENSMUSG00000077274	Gm22786	3.48	4.081	2.95e-08	4.701e-08
ENSMUSG00000026639	Lamb3	4.687	5.48	3.219e-08	5.109e-08
ENSMUSG00000083118	Gm11609	4.722	6.231	3.206e-08	5.095e-08
ENSMUSG00000102447	Gm6621	8.134	4.504	3.128e-08	4.978e-08
ENSMUSG00000064967	Gm24405	4.491	4.642	3.263e-08	5.171e-08
ENSMUSG00000093064	Gm23153	7.862	6.025	3.622e-08	5.731e-08
ENSMUSG00000101846	Gm19927	6.734	5.574	3.672e-08	5.803e-08
ENSMUSG00000101450	Gm28941	5.375	5.851	4.214e-08	6.65e-08
ENSMUSG00000026024	Als2	4.008	4.036	4.684e-08	7.381e-08
ENSMUSG00000101061	Platr1	8.719	5.42	4.956e-08	7.798e-08
ENSMUSG00000100580	4933436I20Rik	9.246	5.651	5.038e-08	7.916e-08
ENSMUSG00000102401	Gm37864	5.906	4.649	5.121e-08	8.035e-08
ENSMUSG00000102245	Gm37713	6.131	4.796	5.175e-08	8.108e-08
ENSMUSG00000104316	Gm37909	5.064	4.684	5.303e-08	8.285e-08
ENSMUSG00000018199	Trove2	3.661	4.209	5.268e-08	8.242e-08
ENSMUSG00000101223	4930568A12Rik	4.759	5.336	5.517e-08	8.596e-08
ENSMUSG00000064389	Gm26446	5.109	3.916	5.339e-08	8.33e-08
ENSMUSG00000026209	Dnpep	8.457	5.152	5.673e-08	8.826e-08
ENSMUSG00000102650	Gm19863	7.237	5.062	5.707e-08	8.867e-08
ENSMUSG00000082935	Gm7658	4.307	4.937	5.932e-08	9.192e-08
ENSMUSG00000026637	Traf5	4.966	3.811	5.733e-08	8.895e-08
ENSMUSG00000026141	Col19a1	5.371	5.099	6.259e-08	9.657e-08
ENSMUSG00000026189	Pecr	5.94	4.857	6.252e-08	9.657e-08
ENSMUSG00000042751	Nmnat2	4.03	4.283	6.18e-08	9.562e-08
ENSMUSG00000094820	Gm23805	4.664	4.797	6.305e-08	9.715e-08
ENSMUSG00000089888	Gm16547	4.487	4.482	7.169e-08	1.103e-07
ENSMUSG00000102508	Gm37367	6.526	4.152	9.599e-08	1.475e-07
ENSMUSG00000051251	Nhlh1	4.677	4.609	1.011e-07	1.552e-07
ENSMUSG00000030768	Disp1	7.378	4.253	1.055e-07	1.616e-07
ENSMUSG00000010453	Kansl3	3.773	4.484	1.079e-07	1.651e-07
ENSMUSG00000067604	Nms	6.834	3.625	1.297e-07	1.983e-07
ENSMUSG00000053963	Stum	5.576	4.674	1.358e-07	2.072e-07
ENSMUSG00000097711	Gm5523	2.716	4.152	1.4e-07	2.134e-07
ENSMUSG00000099843	Gm7160	5.795	5.449	1.424e-07	2.168e-07
ENSMUSG00000025964	Adam23	6.823	4.937	1.461e-07	2.221e-07
ENSMUSG00000102235	Gm37886	8.065	4.623	1.463e-07	2.221e-07
ENSMUSG00000039997	Ifi203	3.217	4.645	1.529e-07	2.317e-07
ENSMUSG00000026458	Ppfia4	5.867	3.878	1.641e-07	2.484e-07
ENSMUSG00000103928	Gm37893	3.419	3.898	1.681e-07	2.541e-07
ENSMUSG00000096992	Gm26788	4.133	4.726	1.748e-07	2.635e-07
ENSMUSG00000101921	Gm28825	5.297	4.51	1.748e-07	2.635e-07
ENSMUSG00000102139	Gm37109	3.442	4.696	1.794e-07	2.701e-07
ENSMUSG00000026036	Nif3l1	5.224	3.9	1.797e-07	2.701e-07
ENSMUSG00000026471	Mr1	7.52	4.175	1.873e-07	2.812e-07
ENSMUSG00000059089	Fcgr4	3.021	4.328	2.035e-07	3.051e-07
ENSMUSG00000006403	Adamts4	3.702	3.74	2.067e-07	3.096e-07
ENSMUSG00000026072	Il1r1	2.959	4.383	2.132e-07	3.188e-07
ENSMUSG00000077939	Gm24133	4.254	4.205	2.365e-07	3.532e-07
ENSMUSG00000045968	Teddm2	6.011	4.564	2.476e-07	3.692e-07
ENSMUSG00000005674	Tomm40l	4.427	4.292	2.505e-07	3.726e-07
ENSMUSG00000026663	Atf6	5.711	4.544	2.517e-07	3.738e-07
ENSMUSG00000100595	Gm19087	7.124	5.345	2.492e-07	3.712e-07
ENSMUSG00000098833	Mir6349	4.551	3.856	2.642e-07	3.919e-07
ENSMUSG00000103780	Gm37524	3.078	4.648	2.663e-07	3.944e-07
ENSMUSG00000090071	Cdk5r2	4.523	4.334	2.713e-07	4.014e-07
ENSMUSG00000079671	2610203C22Rik	3.221	4.425	2.779e-07	4.1e-07
ENSMUSG00000026641	Usf1	3.801	3.693	2.761e-07	4.08e-07
ENSMUSG00000093879	Gm25457	4.885	4.626	2.811e-07	4.142e-07
ENSMUSG00000086706	Gm15848	5.382	4.328	2.86e-07	4.204e-07
ENSMUSG00000064860	n-R5s220	3.235	3.815	2.861e-07	4.204e-07
ENSMUSG00000102413	Gm36937	2.997	4.508	2.89e-07	4.237e-07
ENSMUSG00000103307	Gm19335	6.979	4.632	2.891e-07	4.237e-07
ENSMUSG00000098351	Gm27507	5.658	3.933	3.212e-07	4.702e-07
ENSMUSG00000079554	Aox2	5.103	5.523	3.254e-07	4.757e-07
ENSMUSG00000081261	Gm15370	2.987	3.581	3.392e-07	4.952e-07
ENSMUSG00000101567	Txn-ps1	3.095	5.518	3.425e-07	4.994e-07
ENSMUSG00000026021	Sumo1	3.753	5.007	3.618e-07	5.269e-07
ENSMUSG00000049515	Espnl	3.51	3.657	3.692e-07	5.369e-07
ENSMUSG00000099694	Gm29610	4.97	4.597	3.795e-07	5.512e-07
ENSMUSG00000016262	Sertad4	4.467	4.195	3.924e-07	5.691e-07
ENSMUSG00000056211	R3hdm1	3.433	3.394	3.998e-07	5.792e-07
ENSMUSG00000067017	Gm3608	7.677	4.794	4.102e-07	5.934e-07
ENSMUSG00000037995	Igsf9	9.078	5.256	4.126e-07	5.961e-07
ENSMUSG00000101600	4930599A14Rik	4.175	3.405	4.712e-07	6.791e-07
ENSMUSG00000102194	Gm38169	2.598	4.449	4.839e-07	6.964e-07
ENSMUSG00000100455	Gm29170	7.738	6.789	4.574e-07	6.601e-07
ENSMUSG00000026414	Tnnt2	3.093	3.405	5.734e-07	8.243e-07
ENSMUSG00000026390	Marco	3.927	3.913	5.873e-07	8.431e-07
ENSMUSG00000103101	Gm37048	2.501	3.896	6.002e-07	8.606e-07
ENSMUSG00000084558	Gm24836	3.942	4.263	6.061e-07	8.679e-07
ENSMUSG00000103076	Gm37902	2.243	3.773	6.118e-07	8.748e-07
ENSMUSG00000103288	Gm37273	2.337	3.983	6.485e-07	9.262e-07
ENSMUSG00000036086	Zranb3	2.778	4.065	6.542e-07	9.332e-07
ENSMUSG00000087859	Gm22796	4.454	3.617	7.378e-07	1.051e-06
ENSMUSG00000088291	Gm26412	3.475	3.773	7.438e-07	1.056e-06
ENSMUSG00000099945	1700126A01Rik	4.302	4.405	7.427e-07	1.055e-06
ENSMUSG00000089831	Gm15508	2.161	3.623	7.641e-07	1.083e-06
ENSMUSG00000103736	Gm37650	5.01	4.756	7.724e-07	1.093e-06
ENSMUSG00000104069	Gm37198	3.214	3.605	7.882e-07	1.113e-06
ENSMUSG00000100761	Gm29344	6.178	6.129	7.385e-07	1.051e-06
ENSMUSG00000098306	Gm28040	3.327	3.579	8.557e-07	1.207e-06
ENSMUSG00000026305	Lrrfip1	6.004	5.842	7.835e-07	1.108e-06
ENSMUSG00000042046	Dstyk	6.805	4.451	8.9e-07	1.253e-06
ENSMUSG00000103016	Gm37506	3.839	3.618	9.12e-07	1.283e-06
ENSMUSG00000099922	Gm29016	5.259	3.405	9.196e-07	1.292e-06
ENSMUSG00000026295	Spp2	5.232	4.072	1.019e-06	1.43e-06
ENSMUSG00000026630	Batf3	2.71	3.996	1.025e-06	1.437e-06
ENSMUSG00000093823	Gm24802	3.862	3.215	1.068e-06	1.493e-06
ENSMUSG00000026526	Fh1	6.8	3.464	1.07e-06	1.494e-06
ENSMUSG00000100941	Gm29597	5.249	5.433	1.03e-06	1.441e-06
ENSMUSG00000102897	Gm37853	3.396	4.164	1.152e-06	1.606e-06
ENSMUSG00000102275	Gm37144	3.316	3.576	1.536e-06	2.139e-06
ENSMUSG00000025993	Slc40a1	5.763	4.886	1.565e-06	2.177e-06
ENSMUSG00000091020	Gm5828	4.032	4.814	1.637e-06	2.274e-06
ENSMUSG00000026434	Nucks1	5.471	4.337	1.82e-06	2.525e-06
ENSMUSG00000102612	Gm37845	7.776	4.77	1.855e-06	2.57e-06
ENSMUSG00000025912	Mybl1	5.587	3.686	1.979e-06	2.738e-06
ENSMUSG00000087561	Gm11608	3.019	4.454	2.076e-06	2.87e-06
ENSMUSG00000103382	Gm37755	4.013	3.088	2.298e-06	3.173e-06
ENSMUSG00000077358	Gm26293	3.418	3.084	2.345e-06	3.233e-06
ENSMUSG00000104414	Gm37799	3.098	3.277	2.402e-06	3.307e-06
ENSMUSG00000104424	Gm19058	2.955	3.204	2.503e-06	3.438e-06
ENSMUSG00000099906	Gm28653	4.21	4.704	2.411e-06	3.316e-06
ENSMUSG00000026023	Cdk15	5.022	2.91	2.578e-06	3.532e-06
ENSMUSG00000102299	Gm37539	2.883	4.641	2.57e-06	3.526e-06
ENSMUSG00000052688	Rab7b	3.987	3.201	2.888e-06	3.952e-06
ENSMUSG00000102676	Gm37435	2.055	3.377	3.028e-06	4.139e-06
ENSMUSG00000026280	Atg4b	2.976	2.907	3.155e-06	4.297e-06
ENSMUSG00000083582	Gm15393	3.577	3.277	3.154e-06	4.297e-06
ENSMUSG00000104435	Gm37422	3.143	2.894	3.206e-06	4.351e-06
ENSMUSG00000102133	Gm37106	2.161	3.215	3.202e-06	4.351e-06
ENSMUSG00000037461	Ints7	2.908	3.319	3.205e-06	4.351e-06
ENSMUSG00000103270	Gm37915	2.603	4.486	3.126e-06	4.268e-06
ENSMUSG00000103958	Gm8023	5.24	3.583	3.257e-06	4.414e-06
ENSMUSG00000099377	Gm6159	2.357	3.957	3.403e-06	4.606e-06
ENSMUSG00000097628	Gm2383	2.253	3.386	3.493e-06	4.722e-06
ENSMUSG00000033159	Cnppd1	2.801	3.323	3.524e-06	4.758e-06
ENSMUSG00000026198	Abcb6	1.963	3.209	3.917e-06	5.277e-06
ENSMUSG00000058729	Lin9	6.862	5.163	3.606e-06	4.863e-06
ENSMUSG00000100221	4930532M18Rik	2.114	3.505	4.044e-06	5.441e-06
ENSMUSG00000089670	Gm16581	3.143	3.493	4.256e-06	5.72e-06
ENSMUSG00000100267	Gm28779	3.454	3.55	4.275e-06	5.738e-06
ENSMUSG00000099878	Gm28178	1.963	3.204	4.43e-06	5.938e-06
ENSMUSG00000100977	Gm29334	2.024	3.326	4.556e-06	6.1e-06
ENSMUSG00000067028	Cntnap5b	2.349	3.589	5.207e-06	6.964e-06
ENSMUSG00000026288	Inpp5d	6.491	4.194	5.324e-06	7.111e-06
ENSMUSG00000026439	Rbbp5	2.345	3.377	5.574e-06	7.437e-06
ENSMUSG00000079139	Gm4204	2.802	3.188	6.183e-06	8.24e-06
ENSMUSG00000102839	Gm37385	2.604	3.405	6.275e-06	8.352e-06
ENSMUSG00000041809	Efhc1	3.876	3.617	6.322e-06	8.405e-06
ENSMUSG00000091199	Gm2619	3.273	3.277	6.577e-06	8.733e-06
ENSMUSG00000103984	Gm37447	2.285	3.261	6.589e-06	8.738e-06
ENSMUSG00000101890	Gm3116	2.299	3.302	7.94e-06	1.049e-05
ENSMUSG00000100711	Gm29461	2.731	3.773	7.834e-06	1.037e-05
ENSMUSG00000104111	Gm37294	8.667	5.977	6.743e-06	8.932e-06
ENSMUSG00000085311	Gm15445	3.88	3.464	8.415e-06	1.111e-05
ENSMUSG00000026638	Irf6	6.002	3.261	1.033e-05	1.36e-05
ENSMUSG00000097970	Gm27028	2.884	3.521	1.029e-05	1.356e-05
ENSMUSG00000102090	Gm8964	3.197	4.272	1.166e-05	1.533e-05
ENSMUSG00000102618	Gm37848	7.39	3.692	1.36e-05	1.783e-05
ENSMUSG00000103548	Gm37670	3.563	3.084	1.396e-05	1.825e-05
ENSMUSG00000004707	Ly9	4.045	4.089	1.347e-05	1.768e-05
ENSMUSG00000106377	Gm44300	8.626	4.688	1.338e-05	1.758e-05
ENSMUSG00000073664	Nbeal1	5.735	3.187	1.451e-05	1.895e-05
ENSMUSG00000089790	Gm16588	2.483	3.084	1.502e-05	1.96e-05
ENSMUSG00000098407	Mir6348	4.233	3.36	1.525e-05	1.987e-05
ENSMUSG00000101168	Gm28892	9.329	5.264	1.386e-05	1.815e-05
ENSMUSG00000041670	Rims1	7.78	3.883	1.548e-05	2.015e-05
ENSMUSG00000104410	Gm37066	7.336	3.899	1.619e-05	2.105e-05
ENSMUSG00000100872	1700065J18Rik	6.888	3.607	1.699e-05	2.206e-05
ENSMUSG00000038235	F11r	5.423	3.098	1.776e-05	2.303e-05
ENSMUSG00000039748	Exo1	3.265	3.2	1.803e-05	2.332e-05
ENSMUSG00000097988	Gm10535	2.451	3.405	1.79e-05	2.319e-05
ENSMUSG00000102408	4930568G15Rik	4.924	3.809	1.825e-05	2.358e-05
ENSMUSG00000100777	4930598F16Rik	3.377	3.118	1.999e-05	2.581e-05
ENSMUSG00000070870	Cryge	2.193	3.084	2.199e-05	2.831e-05
ENSMUSG00000077328	Gm25832	3.289	3.884	2.131e-05	2.748e-05
ENSMUSG00000100053	Gm28154	3.046	3.032	2.428e-05	3.12e-05
ENSMUSG00000006007	Pdc	4.365	3.916	2.405e-05	3.093e-05
ENSMUSG00000026525	Opn3	6.452	3.499	2.633e-05	3.379e-05
ENSMUSG00000103516	Gm37431	3.083	3.141	2.765e-05	3.541e-05
ENSMUSG00000026271	Gpr35	4.837	3.858	2.699e-05	3.46e-05
ENSMUSG00000060679	Mrps9	6.77	3.525	2.857e-05	3.649e-05
ENSMUSG00000026430	Rassf5	2.359	2.895	3.065e-05	3.911e-05
ENSMUSG00000026219	Trip12	8.811	4.927	2.775e-05	3.549e-05
ENSMUSG00000103137	Gm37981	2.931	3.012	3.265e-05	4.162e-05
ENSMUSG00000073478	D730003I15Rik	2.959	2.91	3.293e-05	4.192e-05
ENSMUSG00000094497	Gm8210	3.071	2.529	3.571e-05	4.531e-05
ENSMUSG00000087721	Gm26280	5.3	3.652	3.415e-05	4.342e-05
ENSMUSG00000026667	Uhmk1	2.345	3.084	3.534e-05	4.489e-05
ENSMUSG00000089648	Gm15790	4.211	2.706	3.863e-05	4.884e-05
ENSMUSG00000033569	Adgrb3	7.003	3.586	3.712e-05	4.704e-05
ENSMUSG00000086064	4930439A04Rik	2.406	3.228	3.791e-05	4.799e-05
ENSMUSG00000047021	Cfap65	6.864	2.971	3.925e-05	4.952e-05
ENSMUSG00000026628	Atf3	2.864	3.588	3.882e-05	4.904e-05
ENSMUSG00000101955	Gm29376	2.161	2.907	4.111e-05	5.175e-05
ENSMUSG00000026620	Mark1	2.514	3.952	3.977e-05	5.012e-05
ENSMUSG00000102193	Gm7299	3.148	2.703	4.254e-05	5.349e-05
ENSMUSG00000026509	Capn2	5.609	2.971	5.234e-05	6.574e-05
ENSMUSG00000082373	Gm13754	2.71	2.336	5.551e-05	6.956e-05
ENSMUSG00000089057	Gm23349	2.915	3.115	5.527e-05	6.933e-05
ENSMUSG00000100253	1700020N18Rik	2.933	3.78	5.711e-05	7.148e-05
ENSMUSG00000103768	Gm37856	2.008	2.937	6.137e-05	7.664e-05
ENSMUSG00000062510	Nsl1	3.455	3.666	6.09e-05	7.614e-05
ENSMUSG00000026572	Tbx19	3.401	2.656	6.474e-05	8.068e-05
ENSMUSG00000026580	Selp	3.473	2.827	6.54e-05	8.12e-05
ENSMUSG00000032719	Sbspon	2.299	3.274	6.416e-05	8.004e-05
ENSMUSG00000102372	A430050A11Rik	3.421	3.011	6.521e-05	8.108e-05
ENSMUSG00000073490	Ifi207	2.61	3.392	6.518e-05	8.108e-05
ENSMUSG00000026626	Ppp2r5a	4.298	3.182	6.752e-05	8.366e-05
ENSMUSG00000104007	Gm38332	3.307	3.992	6.546e-05	8.12e-05
ENSMUSG00000085653	Gm15179	6.411	3.071	6.997e-05	8.651e-05
ENSMUSG00000103824	Gm38177	5.368	2.656	7.174e-05	8.86e-05
ENSMUSG00000065533	Mir205	5.747	2.944	7.282e-05	8.974e-05
ENSMUSG00000002459	Rgs20	4.755	2.336	7.57e-05	9.307e-05
ENSMUSG00000076999	Gm22426	4.616	3.978	6.969e-05	8.626e-05
ENSMUSG00000101429	BC055402	2.512	2.529	7.642e-05	9.387e-05
ENSMUSG00000104077	Gm37027	3.125	4.017	7.218e-05	8.904e-05
ENSMUSG00000041605	5730559C18Rik	2.069	2.656	7.976e-05	9.775e-05
ENSMUSG00000103706	6820402A03Rik	3.59	3.121	7.812e-05	9.584e-05
ENSMUSG00000100480	Gm29156	5.222	4.247	7.488e-05	9.218e-05
ENSMUSG00000026248	Mrpl44	3.201	2.529	8.756e-05	0.0001071
ENSMUSG00000025930	Msc	3.689	3.831	8.177e-05	0.0001001
ENSMUSG00000026173	Plcd4	3.129	3.013	9.485e-05	0.0001159
ENSMUSG00000087538	Gm16341	2.716	3.037	9.51e-05	0.000116
ENSMUSG00000043263	Ifi209	3.495	2.996	0.0001027	0.0001251
ENSMUSG00000101721	Gm8354	1.995	2.719	0.0001086	0.0001323
ENSMUSG00000026123	Plekhb2	4.174	2.665	0.0001103	0.0001341
ENSMUSG00000103482	Gm37999	4.382	2.529	0.0001138	0.0001379
ENSMUSG00000025779	Ly96	3.46	2.846	0.0001116	0.0001356
ENSMUSG00000104072	4930448I06Rik	3.579	2.703	0.0001135	0.0001378
ENSMUSG00000109599	Gm31812	1.81	2.525	0.0001147	0.0001389
ENSMUSG00000101483	1700016L21Rik	3.093	2.846	0.000122	0.0001474
ENSMUSG00000089420	Gm27934	3.318	2.707	0.0001234	0.0001489
ENSMUSG00000085184	4933439K11Rik	3.497	3.138	0.0001211	0.0001465
ENSMUSG00000056699	Gm5533	4.141	2.923	0.0001342	0.0001616
ENSMUSG00000065435	Mir135b	2.704	3.071	0.0001362	0.0001638
ENSMUSG00000013275	Slc41a1	4.33	4.081	0.0001267	0.0001528
ENSMUSG00000064281	Rpl19-ps1	4.531	2.63	0.0001451	0.0001742
ENSMUSG00000026064	Ptp4a1	3.662	3.045	0.000142	0.0001706
ENSMUSG00000038242	Aox4	3.056	2.823	0.0001495	0.0001792
ENSMUSG00000102608	Gm37267	2.253	2.656	0.0001594	0.0001907
ENSMUSG00000088164	Gm23534	2.625	3.169	0.0001577	0.0001888
ENSMUSG00000047216	Cdh19	6.097	2.95	0.0001664	0.0001989
ENSMUSG00000101378	Gm28086	3.324	2.963	0.0001744	0.0002083
ENSMUSG00000103357	Gm7804	7.026	3.297	0.0001757	0.0002094
ENSMUSG00000101798	Gm28995	7.097	3.334	0.0001755	0.0002093
ENSMUSG00000097845	A230020J21Rik	4.964	4.023	0.0001876	0.0002233
ENSMUSG00000089838	Gm2962	3.747	3.1	0.0002167	0.0002573
ENSMUSG00000088953	Gm24942	2.99	2.897	0.0002278	0.0002702
ENSMUSG00000025934	Gsta3	4.767	4.577	0.0002045	0.0002431
ENSMUSG00000101523	Gm10031	3.037	3.173	0.0002312	0.000274
ENSMUSG00000103999	Gm38026	2.299	2.703	0.0002468	0.0002918
ENSMUSG00000102982	Gm38319	2.101	2.529	0.0002563	0.0003025
ENSMUSG00000104423	A030005K14Rik	4.315	2.336	0.0002614	0.0003079
ENSMUSG00000097608	Gm7952	3.197	2.336	0.0002656	0.0003125
ENSMUSG00000101939	Gm28438	2.964	3.039	0.0002588	0.0003051
ENSMUSG00000026029	Casp8	3.729	3.521	0.0002556	0.0003019
ENSMUSG00000099592	Gm5264	2.299	2.725	0.0002696	0.0003169
ENSMUSG00000100831	Gm17847	1.718	2.336	0.0002805	0.0003289
ENSMUSG00000026475	Rgs16	1.718	2.336	0.0002805	0.0003289
ENSMUSG00000099568	Gm28513	9.099	4.81	0.0002339	0.0002769
ENSMUSG00000102254	Gm7496	2.193	2.657	0.0002857	0.0003343
ENSMUSG00000090246	Gm17017	3.012	3.493	0.000284	0.0003327
ENSMUSG00000101154	Gm17777	5.696	2.529	0.0003065	0.0003579
ENSMUSG00000025900	Rp1	2.055	2.786	0.0003018	0.0003527
ENSMUSG00000088813	Gm22383	2.962	3.1	0.0003185	0.0003715
ENSMUSG00000047180	Neurl3	2.285	2.128	0.0003789	0.0004415
ENSMUSG00000103089	Gm38118	2.684	3.137	0.0004039	0.0004697
ENSMUSG00000051985	Igfn1	3.287	2.86	0.000418	0.0004856
ENSMUSG00000026147	Col9a1	3.351	3.704	0.0004034	0.0004696
ENSMUSG00000090222	Ifi203-ps	2.116	2.651	0.0004403	0.0005109
ENSMUSG00000026565	Pou2f1	1.963	2.461	0.0004514	0.0005227
ENSMUSG00000026035	Ppil3	1.718	2.311	0.0004712	0.0005451
ENSMUSG00000026125	Prss39	2.736	3.037	0.0004506	0.0005223
ENSMUSG00000026567	Adcy10	2.101	2.121	0.0005139	0.0005938
ENSMUSG00000103280	Gm37277	2.992	1.933	0.0005605	0.000647
ENSMUSG00000045515	Pou3f3	4.26	1.933	0.0005925	0.0006826
ENSMUSG00000064845	Gm26203	2.273	2.507	0.0005851	0.0006748
ENSMUSG00000100635	Gm29157	2.467	1.931	0.0006729	0.0007743
ENSMUSG00000041926	Rnpep	2.207	1.933	0.0006793	0.0007801
ENSMUSG00000103443	Gm37132	2.253	2.121	0.0006789	0.0007801
ENSMUSG00000097893	1700034P13Rik	2.193	2.112	0.0006873	0.0007885
ENSMUSG00000104164	Gm38248	4.188	2.311	0.0007006	0.0008029
ENSMUSG00000103510	Gm30725	2.451	2.514	0.0007265	0.0008317
ENSMUSG00000100561	Gm7516	5.214	1.931	0.000798	0.0009099
ENSMUSG00000103492	Gm37412	2.253	1.931	0.000812	0.0009233
ENSMUSG00000102499	Gm36984	5.608	1.931	0.0008123	0.0009233
ENSMUSG00000101126	Gm10538	2.559	2.665	0.000798	0.0009099
ENSMUSG00000104121	Gm37485	4.584	2.638	0.0008049	0.0009168
ENSMUSG00000079434	Neu2	4.258	3.209	0.0007807	0.0008929
ENSMUSG00000100850	4930434B07Rik	2.914	3.239	0.0007833	0.000895
ENSMUSG00000103560	Gm38070	2.467	2.121	0.0008592	0.0009757
ENSMUSG00000041559	Fmod	2.889	2.289	0.0009363	0.001062
ENSMUSG00000099835	Gm29668	2.314	1.931	0.0009501	0.001076
ENSMUSG00000097797	Gm26901	2.451	2.631	0.000941	0.001066
ENSMUSG00000100269	Gm28782	4.157	1.933	0.0009746	0.001102
ENSMUSG00000026670	Uap1	4.445	2.121	0.0009852	0.001113
ENSMUSG00000060244	Alyref2	2.962	2.112	0.000996	0.001124
ENSMUSG00000104248	Gm38152	1.81	1.933	0.001005	0.001133
ENSMUSG00000049598	Vsig8	3.625	2.107	0.001013	0.00114
ENSMUSG00000100701	Gm19587	2.253	1.931	0.001067	0.001199
ENSMUSG00000102266	Gm38106	6.235	3.48	0.00101	0.001138
ENSMUSG00000070645	Ren1	4.255	1.931	0.001144	0.001283
ENSMUSG00000036104	Rab3gap1	1.718	1.933	0.001203	0.001345
ENSMUSG00000051590	Map3k19	6.129	2.536	0.001179	0.001321
ENSMUSG00000054493	Gm9947	2.58	2.289	0.001197	0.00134
ENSMUSG00000026175	Vil1	3.404	2.097	0.001222	0.001366
ENSMUSG00000104416	Gm37067	1.718	1.933	0.00126	0.001407
ENSMUSG00000103756	Gm37285	2.71	1.931	0.001277	0.001423
ENSMUSG00000100503	Gm28927	2.735	1.933	0.001279	0.001424
ENSMUSG00000077759	Gm23528	2.359	1.931	0.001284	0.001428
ENSMUSG00000103374	Gm37239	2.137	2.581	0.001269	0.001415
ENSMUSG00000026700	Tnfsf4	5.333	2.107	0.001342	0.00149
ENSMUSG00000102956	Gm37017	2.314	1.933	0.001382	0.001532
ENSMUSG00000089866	Gm15699	5.432	2.329	0.001376	0.001527
ENSMUSG00000102134	Gm9931	5.209	2.22	0.001422	0.001575
ENSMUSG00000102414	Gm36938	4.403	5.443	0.001093	0.001227
ENSMUSG00000082059	Gm15183	5.383	2.114	0.001602	0.001772
ENSMUSG00000101945	Gm29138	3.046	1.933	0.001692	0.001868
ENSMUSG00000104157	Gm38101	3.574	2.114	0.001683	0.00186
ENSMUSG00000038218	Gm9765	2.299	1.933	0.001705	0.00188
ENSMUSG00000102845	Gm38033	1.81	1.536	0.001722	0.001896
ENSMUSG00000104366	4933409D19Rik	2.299	1.933	0.001732	0.001905
ENSMUSG00000103272	Gm37914	4.257	2.569	0.001708	0.001882
ENSMUSG00000104379	Gm37509	2.527	2.201	0.001772	0.001947
ENSMUSG00000104291	A130071D04Rik	3.35	2.112	0.001941	0.00213
ENSMUSG00000101337	Dnah7c	4.616	1.933	0.001961	0.00215
ENSMUSG00000070489	4930527J03Rik	1.963	1.933	0.002016	0.002206
ENSMUSG00000025927	Tfap2b	6.074	2.415	0.001987	0.002177
ENSMUSG00000090243	Gm16103	1.902	1.933	0.002066	0.002259
ENSMUSG00000026544	Dusp23	2.823	1.72	0.002121	0.002316
ENSMUSG00000041782	Lad1	3.429	1.536	0.002209	0.00241
ENSMUSG00000097615	Gm2061	2.705	2.112	0.002232	0.002433
ENSMUSG00000103953	Gm29718	3.059	2.201	0.00225	0.00245
ENSMUSG00000104097	Gm37313	2.055	1.536	0.002409	0.002618
ENSMUSG00000041889	Shisa4	1.902	2.097	0.002447	0.002654
ENSMUSG00000006546	Cryba2	2.834	2.289	0.002446	0.002654
ENSMUSG00000104397	Gm37958	2.477	1.536	0.002479	0.002683
ENSMUSG00000094768	Gm25524	2.055	2.289	0.002463	0.002669
ENSMUSG00000095942	Gm25198	2.943	1.536	0.002497	0.002701
ENSMUSG00000099208	Gm27940	2.208	1.536	0.002513	0.002715
ENSMUSG00000102915	Gm32460	5.557	3.462	0.002279	0.00248
ENSMUSG00000102427	Gm37463	3.093	1.536	0.002614	0.002821
ENSMUSG00000103591	Gm38365	2.055	2.128	0.002668	0.002877
ENSMUSG00000065468	Mir26b	2.008	1.933	0.002743	0.002955
ENSMUSG00000104010	Gm37366	2.63	1.536	0.002794	0.003005
ENSMUSG00000033740	St18	6.468	2.31	0.00278	0.002992
ENSMUSG00000073650	Catip	1.963	1.933	0.002979	0.0032
ENSMUSG00000067879	3110035E14Rik	5.115	2.131	0.003043	0.003266
ENSMUSG00000093155	Gm25035	2.42	1.536	0.003132	0.003358
ENSMUSG00000099784	Dalir	2.649	1.536	0.003186	0.003409
ENSMUSG00000102491	Gm36986	2.405	2.311	0.003235	0.003459
ENSMUSG00000102521	Gm37781	2.055	1.536	0.003333	0.003557
ENSMUSG00000103679	Gm37623	2.959	2.738	0.003184	0.003409
ENSMUSG00000070594	Gm4788	2.586	2.336	0.003346	0.003567
ENSMUSG00000104165	Gm38249	1.995	1.931	0.003402	0.003623
ENSMUSG00000084228	Gm8080	2.161	2.78	0.003267	0.003489
ENSMUSG00000101942	Gm19582	2.055	2.121	0.003581	0.00381
ENSMUSG00000043429	Ccdc185	3.413	1.536	0.003671	0.003902
ENSMUSG00000040265	Dnm3	1.995	1.536	0.003716	0.003946
ENSMUSG00000016181	Diexf	1.963	1.536	0.00372	0.003947
ENSMUSG00000104433	Gm37426	2.923	1.72	0.003816	0.004044
ENSMUSG00000042111	Ccdc115	2.766	1.933	0.003822	0.004047
ENSMUSG00000025959	Klf7	3.353	1.72	0.00395	0.004178
ENSMUSG00000077413	Gm23389	4.311	1.536	0.004026	0.004255
ENSMUSG00000097014	Gm26706	3.844	1.536	0.004201	0.004435
ENSMUSG00000026436	Elk4	1.963	1.536	0.004211	0.004438
ENSMUSG00000026596	Abl2	1.963	1.536	0.004211	0.004438
ENSMUSG00000082769	Gm15793	1.963	1.931	0.004219	0.004442
ENSMUSG00000039354	Smarcal1	4.46	1.536	0.004236	0.004456
ENSMUSG00000102374	Gm38387	4.431	1.536	0.004317	0.004537
ENSMUSG00000078193	Gm2000	3.371	1.536	0.004467	0.004686
ENSMUSG00000091937	Gm3646	5.695	2.311	0.004436	0.004657
ENSMUSG00000103963	Gm30932	2.989	1.536	0.004766	0.004995
ENSMUSG00000034220	Gpc1	2.008	1.933	0.004774	0.004998
ENSMUSG00000103085	Gm38120	1.963	1.536	0.004783	0.005003
ENSMUSG00000101702	1700072G22Rik	2.161	1.536	0.004808	0.005024
ENSMUSG00000097416	Gm26670	2.055	1.536	0.004817	0.005029
ENSMUSG00000102331	Gm19938	3.324	1.536	0.004893	0.005104
ENSMUSG00000089291	Gm23208	2.101	1.536	0.004924	0.005131
ENSMUSG00000101617	Gm6947	2.859	1.536	0.004983	0.005188
ENSMUSG00000099227	Mir8114	2.243	1.935	0.005015	0.005216
ENSMUSG00000104253	Gm37174	2.76	1.536	0.005055	0.005253
ENSMUSG00000044757	Gm6430	2.752	1.72	0.00519	0.005388
ENSMUSG00000052759	Gpr25	3.181	1.931	0.005195	0.005388
ENSMUSG00000104474	Gm38210	2.197	1.536	0.005243	0.005434
ENSMUSG00000113925	Gm7544	2.243	1.536	0.005287	0.005474
ENSMUSG00000032883	Acsl3	3.98	1.72	0.005328	0.005511
ENSMUSG00000103114	Gm32200	2.045	1.536	0.005367	0.005546
ENSMUSG00000097251	5033417F24Rik	2.229	1.536	0.005398	0.005574
ENSMUSG00000026502	Desi2	2.99	1.536	0.005449	0.00562
ENSMUSG00000085391	Gm16150	2.906	2.201	0.005503	0.005671
ENSMUSG00000050296	Abca12	4.106	1.536	0.005707	0.005876
ENSMUSG00000102386	2900022M07Rik	3.029	1.72	0.00573	0.005894
ENSMUSG00000109887	Gm28756	3.033	1.536	0.005969	0.006134
ENSMUSG00000102546	Gm9722	2.533	1.536	0.006008	0.006164
ENSMUSG00000104136	Gm36955	2.024	1.931	0.006009	0.006164
ENSMUSG00000103139	Gm37982	4.274	1.536	0.006214	0.006369
ENSMUSG00000064395	Gm23623	2.799	1.842	0.006493	0.006648
ENSMUSG00000103711	Tstd1	3.057	1.536	0.006804	0.00696
ENSMUSG00000025909	Sntg1	4.644	1.536	0.006815	0.006965
ENSMUSG00000099858	Gm6652	3.706	1.536	0.007078	0.007228
ENSMUSG00000104027	Gm37211	2.114	1.536	0.007224	0.007369
ENSMUSG00000042349	Ikbke	4.145	2.097	0.007244	0.007383
ENSMUSG00000100798	Gm19589	2.33	2.131	0.007903	0.008047
ENSMUSG00000026632	Tatdn3	2.197	2.107	0.008144	0.008285
ENSMUSG00000026208	Des	2.396	1.842	0.00821	0.008345
ENSMUSG00000099490	Gm18802	2.488	1.536	0.008321	0.00845
ENSMUSG00000096772	Gm22114	3.421	1.536	0.008461	0.008584
ENSMUSG00000057715	A830018L16Rik	4.971	1.931	0.008831	0.008952
ENSMUSG00000026558	Uck2	2.151	1.536	0.009278	0.009396
ENSMUSG00000103329	Gm42492	4.391	1.72	0.01088	0.01101
ENSMUSG00000100454	Gm28901	3.402	2.254	0.01121	0.01133
ENSMUSG00000103367	Gm38158	3.972	1.536	0.01186	0.01198
ENSMUSG00000100555	Gm8173	2.276	1.536	0.01214	0.01225
ENSMUSG00000103509	Gm38372	3.178	1.536	0.01354	0.01365
ENSMUSG00000089808	Gm7241	3.592	1.536	0.01645	0.01656
ENSMUSG00000044337	Ackr3	2.463	1.931	0.01773	0.01783
ENSMUSG00000026254	Eif4e2	5.314	3.038	0.01642	0.01654
ENSMUSG00000047369	Dnah14	4.281	1.536	0.01786	0.01794
ENSMUSG00000026604	Ptpn14	2.596	1.536	0.02086	0.02093
ENSMUSG00000026368	F13b	2.596	1.536	0.02637	0.02644

plotly::ggplotly(
countData %>% 
  dplyr::filter(., rownames(countData) %in% "ENSMUSG00000034066") %>%
  t() %>%
  as.data.frame() %>%
  tibble::rownames_to_column("samplename") %>%
  dplyr::select(., samplename, ENSMUSG00000034066 = V1) %>%
  left_join(., sampleGroups, by = "samplename") %>%
  ggplot() +
  geom_boxplot(aes(x = group, y = ENSMUSG00000034066,
                   fill = group)) +
  theme_bw()
)

Box plot showing mean differences in our gene of interest between tissue types.